Goodhart's law

How to Read Numbers: A Guide to Statistics in the News (And Knowing When to Trust Them)
by Tom Chivers and David Chivers
Published 18 Mar 2021

And people in the media can lose sight of it as well, so we get press releases about the number of ‘items of PPE’ produced, not caring about whether each ‘item’ is an N95 respirator mask or a single rubber glove.13 There are ways around Goodhart’s law, to some degree: changing metrics frequently, or assessing things using multiple metrics, can mitigate it. But no measurement will ever fully capture the underlying reality, which is always more complicated. ‘Looking for the perfect summary statistic,’ as the author Will Kurt once tweeted, ‘is like trying to write a dust jacket blurb that replaces the need to read the book.’14 Obviously enough, that’s what happened with the 100,000 tests target. (This isn’t hindsight: Tom wrote ahead of the announcement that it was ‘a hotbed for Goodhart’s law’.15) The idea – and it was a noble one – was to set a target which would, like the bonus for car sales, drive up the number of tests.

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‘Sir David Norgrove response to Matt Hancock regarding the government’s COVID-19 testing data’, UK Statistics Authority, 2 June 2020 https://www.statisticsauthority.gov.uk/correspondence/sir-david-norgrove-response-to-matt-hancock-regarding-the-governments-covid-19-testing-data/ 9. Daisy Christodoulou, ‘Exams and Goodhart’s law’, 2013 https://daisychristodoulou.com/2013/11/exams-and-goodharts-law/ 10. Dave Philipps, ‘At veterans hospital in Oregon, a push for better ratings puts patients at risk, doctors say’, the New York Times, 2018 https://www.nytimes.com/2018/01/01/us/at-veterans-hospital-in-oregon-a-push-for-better-ratings-puts-patients-at-risk-doctors-say.html 11.

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Chapter 13: Rankings Chapter 14: Is It Representative of the Literature? Chapter 15: Demand for Novelty Chapter 16: Cherry-picking Chapter 17: Forecasting Chapter 18: Assumptions in Models Chapter 19: Texas Sharpshooter Fallacy Chapter 20: Survivorship Bias Chapter 21: Collider Bias Chapter 22: Goodhart’s Law Conclusion and Statistical Style Guide Acknowledgements Notes Also by Tom Chivers Copyright Introduction Numbers do not feel. Do not bleed or weep or hope. They do not know bravery or sacrifice. Love and allegiance. At the very apex of callousness, you will find only ones and zeros.

Cogs and Monsters: What Economics Is, and What It Should Be
by Diane Coyle
Published 11 Oct 2021

The financial deregulation and innovation meant that the economic meaning of any given measure and growth rate of the money supply was unclear. What’s more, the act of using policy levers to target the growth of any specific monetary aggregate also induced changes in people’s behaviour that made that aggregate irrelevant for the wider policy aim—in this context, this is known as Goodhart’s Law, which states that the act of targeting a variable eliminates the information that made it a useful policy indicator in the first place. As Charles Goodhart expressed it, ‘Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes’ (Goodhart 1975, 122).

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My job as a very junior economist in the Treasury in 1985–86 included the dull task of constructing a variety of new monetary aggregates and calculating which had the slowest growth rate. This slower-growing new measure (named PSLX in my computer programme) joined the earlier official targets in the next Budget, although it also subsequently joined them in their unwelcome exuberance. It lived up to Goodhart’s Law, as its growth accelerated as soon as it became an official policy target (renamed PSL2). The point of this anecdote is that the refraction of an intellectual trend in academic economics through the political process sometimes leads to a set of ideas being too dominant and too long-lived in the policy world after the academic bandwagon has rolled on.

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Policy in Wonderland What does all this mean for the practicalities of drawing up policies and assessing policy impact? As Chapter One argued, economists in practice tend to ignore the consequences of economics being a social science, involving sentient beings who—all too often—change their behaviour in response to policy changes, or even policy debate. Of course, economists know this. While we have Goodhart’s Law described earlier (that targeting a variable changes its behaviour), in the context of macroeconomics we also have the Lucas Critique (stating that historical relationships are no guide to the future when there are structural changes in the economy such as a new technology or different labour laws), but too often ignore the implications.

The Skeptical Economist: Revealing the Ethics Inside Economics
by Jonathan Aldred
Published 1 Jan 2009

Any government adopting an explicit, overarching policy of maximizing perceived happiness must confront its relationship with democratic politics. As I have argued, the Greatest Happiness principle cannot stand above politics. Another problem is what economists call Goodhart’s Law.9 A succinct definition is: ‘when a measure becomes a target, it ceases to be a good measure’. So once governments target aggregate measures of self-reported happiness, these measures cease to track ‘true’ happiness. Goodhart’s Law can be thought of as the application to human society of Heisenberg’s Uncertainty Principle in quantum physics. Put simply, measuring a system generally disturbs it.60 In human society, an important reason is that people manipulate data once it matters to them.

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In general, Medicare involves fee-for-service payments to doctors; this means that bad doctors are paid more than good ones, because Medicare pays doctors to fix the mistakes they have previously made. There is an optimistic view that these kinds of problems can be avoided by carefully chosen targets and incentive structures, but this view is not supported by most independent reviews of the audit culture.23 On the contrary, Goodhart’s Law (introduced in Chapter 5) is frequently mentioned: when a measure becomes a target, it ceases to be a good measure. Goodhart’s Law suggests that distortions and unintended consequences are an unavoidable part of any target regime. Once people or their activities are being targeted, their behaviour inevitably changes, either unintentionally or because they actively seek to manipulate the measurements.

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It is then difficult or impossible to measure individual performance, but if the performance of the team as a whole is rewarded, this creates an incentive for team members to rely on the effort of their colleagues. Another problem is that a worker may serve many masters, each of whom has different views about which aspects of the job matter most. Taken together, the problems posed by Goodhart’s Law, multitasking, team working, multiple masters, and the fundamental difficulty of defining and measuring a qualitative ‘output’ greatly limit the applicability of PRP schemes. More generally, these problems undermine any system of targets supported by financial carrots and sticks. The problems with the audit culture are not specific to the public sector; they arise in the commercial world too.

The Choice Factory: 25 Behavioural Biases That Influence What We Buy
by Richard Shotton
Published 12 Feb 2018

Contentsx Praise for The Choice Factory Preface Introduction Bias 1: The Fundamental Attribution Error Bias 2: Social Proof Bias 3: Negative Social Proof Bias 4: Distinctiveness Bias 5: Habit Bias 6: The Pain of Payment Bias 7: The Danger of Claimed Data Bias 8: Mood Bias 9: Price Relativity Bias 10: Primacy Effect Bias 11: Expectancy Theory Bias 12: Confirmation Bias Bias 13: Overconfidence Bias 14: Wishful Seeing Bias 15: Media Context Bias 16: The Curse of Knowledge Bias 17: Goodhart’s Law Bias 18: The Pratfall Effect Bias 19: Winner’s Curse Bias 20: The Power of the Group Bias 21: Veblen Goods Bias 22: The Replicability Crisis Bias 23: Variability Bias 24: Cocktail Party Effect Bias 25: Scarcity Ethics Conclusion References Further reading Acknowledgements Index Praise for The Choice Factory “This book is a Haynes Manual for understanding consumer behaviour.

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Insight work doesn’t need to be expensive and it doesn’t need to be complex. There’s no excuse not to better understand the buyer. However, not all tracking data helps you better understand your audience. Sometimes data can be downright misleading if not interpreted correctly. We’ll address that issue in the next chapter. Bias 17: Goodhart’s Law The danger of poorly set digital targets Today is the last working day of the quarter and you need to register a large sale to hit your target. If you manage, you’ll receive your bonus. That should be simple, as one of your longest-standing clients has promised to place an order today.

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You could have doubled your company’s income from the sale by waiting, but that would have jeopardised your bonus. From your perspective this was a rational decision, but it’s counter to your employer’s intention. They created the bonus system to boost income, but in this case it has reduced it. This poorly set target, which led to unintended consequences, is an example of Goodhart’s Law. This states: When a measure becomes a target, it ceases to be a good measure. One infamous example of unintended consequences is from Hanoi, Vietnam, during the spring of 1902. When faced with an outbreak of bubonic plague, the French colonialists offered a small fee for every rat’s tail handed in.

Calling Bullshit: The Art of Scepticism in a Data-Driven World
by Jevin D. West and Carl T. Bergstrom
Published 3 Aug 2020

What had actually happened was that a number of US states lost jobs overall, while others gained. These changes nearly balanced out, leaving a net change of only about 18,000 jobs. Wisconsin’s net growth of 9,500 jobs was over half the size of the country’s net growth, even though only a small fraction of the total jobs created in the country were created in Wisconsin. GOODHART’S LAW When scientists measure the molecular weights of the elements, the elements do not conspire to make themselves heavier and connive to sneak down the periodic table. But when administrators measure the productivity of their employees, they cannot expect these people to stand by idly: Employees want to look good.

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To lift their average SAT scores, schools employed a range of stratagems: not requiring SAT for international applicants who typically have lower scores, bringing in lower-scoring students in the spring semester when their scores are not counted, or even paying admitted students to retake the SAT, with bonuses for substantive increases in score. These efforts to game the system undercut the value of the rankings. The rankings ended up being influenced as much by admissions departments’ willingness to chase metrics as they did by the quality of schools’ applicants. This problem is canonized in a principle known as Goodhart’s law. While Goodhart’s original formulation is a bit opaque,*8 anthropologist Marilyn Strathern rephrased it clearly and concisely: When a measure becomes a target, it ceases to be a good measure. In other words, if sufficient rewards are attached to some measure, people will find ways to increase their scores one way or another, and in doing so will undercut the value of the measure for assessing what it was originally designed to assess.

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But once the number of cars sold becomes a target, salespeople alter their sales strategies, more cars will be sold, and yet more cars sold will not necessarily correspond to higher profits as salespeople offer bigger discounts to make sales and make them quickly. At around the same time that Goodhart proposed his law, psychologist Donald Campbell independently proposed an analogous principle: The more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.

Super Thinking: The Big Book of Mental Models
by Gabriel Weinberg and Lauren McCann
Published 17 Jun 2019

(We’re married.) Though we discuss a wide array of topics, we often find common threads—recurring concepts that help us explain, predict, or approach these seemingly disparate subjects. Examples range from more familiar concepts, such as opportunity cost and inertia, to more obscure ones, such as Goodhart’s law and regulatory capture. (We will explain these important ideas and many more in the pages that follow.) These recurring concepts are called mental models. Once you are familiar with them, you can use them to quickly create a mental picture of a situation, which becomes a model that you can later apply in similar situations.

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If the output of nails was determined by their number, factories produced huge numbers of pinlike nails; if by weight, smaller numbers of very heavy nails. The satiric magazine Krokodil once ran a cartoon of a factory manager proudly displaying his record output, a single gigantic nail suspended from a crane. Goodhart’s law summarizes the issue: When a measure becomes a target, it ceases to be a good measure. This more common phrasing is from Cambridge anthropologist Marilyn Strathern in her 1997 paper “‘Improving Ratings’: Audit in the British University System.” However, the “law” is named after English economist Charles Goodhart, whose original formulation in a conference paper presented at the Reserve Bank of Australia in 1975 stated: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”

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In Hanoi, the local government created a bounty program for rats, paying the bounty based on a rat’s tail. Enterprising ratcatchers, however, would catch and release the rats after just cutting off their tails; that way the rats could go back and reproduce. Whenever you create an incentive structure, you must heed Goodhart’s law and watch out for perverse incentives, lest you be overrun by cobras and rats! The Streisand effect applies to an even more specific situation: when you unintentionally draw more attention to something when you try to hide it. It’s named for entertainer Barbra Streisand, who sued a photographer and website in 2003 for displaying an aerial photo of her mansion, which she wanted to remain private.

The Geek Way: The Radical Mindset That Drives Extraordinary Results
by Andrew McAfee
Published 14 Nov 2023

Wells Fargo paid more than $2.5 billion in fines over its false account scandal. The endless supply of examples like these has led some people to believe in Goodhart’s law, which states that “when a measure becomes a target, it ceases to be a good measure.” In other words, getting people to pay close attention to a metric—by, for example, tying their compensation to it—is almost sure to backfire somehow. Yet most of the business geeks I’ve talked to don’t let Goodhart’s law get in the way of their mania for measurement and all the things that accompany it—observation, experimentation, analysis, argumentation, and so on.

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When I asked if he was worried about Goodhart’s law, he replied, “Univariate metrics are almost always inadequate, so for any area across Stripe we have our primary metrics. And we also try to choose counterbalancing metrics to control for or assess the most obvious kind of pathologies that could arise if you only optimized one of them. And we also have all sorts of secondary metrics that we just have to keep an eye on. And with like fifteen metrics on this product or area of our business or whatever, I don’t worry too much about Goodhart.” As Collison highlights, a key part of the geek response to Goodhart’s law is to pay attention to an entire dashboard of metrics, instead of any single number.

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,” Knowledge at Wharton, accessed February 14, 2023, https://knowledge.wharton.upenn.edu/article/how-serious-was-the-fraud-at-computer-associates/#:~:text=They%20paid%20more%20than%20they,once%20the%20practices%20were%20revealed. 66 SEC filed securities fraud charges: “SEC Files Securities Fraud Charges Against Computer Associates International, Inc., Former CEO Sanjay Kumar, and Two Other Former Company Executives,” SEC, accessed February 14, 2023, www.sec.gov/news/press/2004-134.htm. 67 Kumar, who had been Computer Associates’ CEO: Michael J. de la Merced, “Ex-Leader of Computer Associates Gets 12-Year Sentence and Fine,” New York Times, November 3, 2006, www.nytimes.com/2006/11/03/technology/03computer.html. 68 hard-charging sales culture: Randall Smith, “Copying Wells Fargo, Banks Try Hard Sell,” Wall Street Journal, February 28, 2011, www.wsj.com/articles/SB10001424052748704430304576170702480420980. 69 12,000 new products: Bethany McLean, “How Wells Fargo’s Cutthroat Corporate Culture Allegedly Drove Bankers to Fraud,” Vanity Fair, May 31, 2017, www.vanityfair.com/news/2017/05/wells-fargo-corporate-culture-fraud. 70 3.5 million fake accounts: Kevin McCoy, “Wells Fargo Review Finds 1.4m More Potentially Unauthorized Accounts,” USA Today, August 31, 2017, www.usatoday.com/story/money/2017/08/31/wells-fargo-review-finds-1-4-m-more-unauthorized-accounts/619794001/. 71 more than $2.5 billion in fines: “Attorney General Shapiro Announces $575 Million 50-State Settlement with Wells Fargo Bank for Opening Unauthorized Accounts and Charging Consumers for Unnecessary Auto Insurance, Mortgage Fees,” Pennsylvania Office of Attorney General, December 28, 2018, www.attorneygeneral.gov/taking-action/attorney-general-shapiro-announces-575-million-50-state-settlement-with-wells-fargo-bank-for-opening-unauthorized-accounts-and-charging-consumers-for-unnecessary-auto-insurance-mortgage-fees/. 72 “when a measure becomes a target”: Michael F Stumborg, Timothy D. Blasius, Steven J. Full, and Christine A. Hughes, “Goodhart’s Law: Recognizing and Mitigating the Manipulation of Measures in Analysis,” CNA, September 2022, www.cna.org/reports/2022/09/goodharts-law#:~:text=Goodhart%27s%20Law%20states%20that%20%E2%80%9Cwhen,order%20to%20receive%20the%20reward. 73 tweeted a photo of himself: Patrick Collison (@patrickc), “Hit our engagement metrics this weekend! ,” Twitter, June 23, 2019, 8:33 p.m., https://twitter.com/patrickc/status/1142953801969573889?

Restarting the Future: How to Fix the Intangible Economy
by Jonathan Haskel and Stian Westlake
Published 4 Apr 2022

But the societal benefits of research are very hard to measure ex ante, so in practice a government measures other things, such as the quality of the researchers’ grant applications, the researchers’ publication record, metrics about the institution they work for, and a host of other variables that approximate “expected societal benefits” but are at best an imperfect proxy. In the mid-1970s, the psychologist Donald Campbell and the economist Charles Goodhart came up with eponymous laws to the effect that quantified incentive systems always lead to perverse outcomes.11 That is, targeting a chosen measure will end up corrupting that measure. Campbell’s law and Goodhart’s law certainly seem to come into play in the field of public research funding. The so-called metric tide, through which researchers and research funding in rich countries become subject to ever more sophisticated performance management and appraisal processes, is widely viewed as a mixed blessing at best.

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Universities typically have a strong interest in receiving fees in return for educating students but much weaker incentives to ensure that students actually learn things that will be useful in later life. Governments sometimes try to solve these problems with more rules and metrics, which can help—but then we are back to Campbell’s law and Goodhart’s law. The Second Paradox: Blackberries and “Blurred Lines”—the Dilemma over IP The other way that governments try to mitigate the problem of intangible spillovers is through IPRs, in particular patents and copyrights. Here again there is a dilemma. The basic idea behind IPRs is straightforward.

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On occupational licencing, the economist Morris Kleiner, for example, has documented higher prices but no higher quality in US states with more restrictions on dentists and mortgage brokers.23 What’s more, IP rules run the same three risks that systems for providing public intangible investment do. Goodhart’s law manifests itself when innovators focus on gaming IP rules rather than innovating. The challenges that early hip-hop artists faced from music rights holders are an example of how an IP regime that is basically fit for purpose can become much less effective when technology changes (in this case, not the physical technology of sampling but rather the “aesthetic technology” of the musical style itself).

Where Does Money Come From?: A Guide to the UK Monetary & Banking System
by Josh Ryan-Collins , Tony Greenham , Richard Werner and Andrew Jackson
Published 14 Apr 2012

Twentieth century: the decline of gold, deregulation and the rise of digital money 3.6.1. A brief history of exchange rate regimes 3.6.2. WWI, the abandonment of the gold standard and the regulation of credit 3.6.3. Deregulation of the banking sector in the 1970s and 1980s 3.6.4. The emergence of digital money 4. MONEY AND BANKING TODAY 4.1. Liquidity, Goodhart’s law, and the problem of defining money 4.2. Banks as the creators of money as credit 4.3. Payment: using central bank reserves for interbank payment 4.3.1. Interbank clearing: reducing the need for central bank reserves 4.3.2. Effects on the money supply 4.4. Cash and seignorage 4.4.1. Is cash a source of ‘debt-free’ money?

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In particular, we need to understand that there are some constraints on the quantity of credit that banks can create even though they effectively have a licence to create new money. The next two chapters provide a detailed overview of the way that commercial banking works today in the UK and how it interacts with the central bank, the payment system and the money markets. To start, we need to address the concept of liquidity. 4.1. Liquidity, Goodhart’s law, and the problem of defining money In Section 3.1, we saw that acceptability as a means of exchange and of final settlement were key functions of money. History suggests that one useful way of judging acceptability of money is whether you can use it to pay taxes and, more generally across the economy, to buy goods and services.

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Further complications arise from the capacity of modern capitalism to continuously create new forms of credit/debt that are not perfectly liquid.3, 4 Economist Charles Goodhart argued that defining money was inherently problematic because whenever a particular instrument or asset was publicly defined as money by an authority in order to better control it, substitutes were produced for the purposes of evasion5 (this is known as ‘Goodhart’s law’).6 We revisit the question of where to draw the line around ‘money’ later in Chapters 5 and 7. The process of defining money becomes easier when we focus on the question of when and how new money is created. Then the definitional problems become irrelevant. When a bank makes a loan it invariably credits the borrower’s current account and from there the money is spent.

The Price of Time: The Real Story of Interest
by Edward Chancellor
Published 15 Aug 2022

Yet only six years later the United States was facing the most severe financial crisis in decades. As the newly installed Chairman of the Federal Reserve, Bernanke found himself in the position to make good on this promise. He wasn’t going to make the same mistakes again. Part Two * * * HOW LOW RATES BEGOT LOWER RATES 7 Goodhart’s Law When a measure becomes a target, it ceases to be a good measure. Goodhart’s Law We have not targeted those things which we ought to have targeted, and we have targeted those things which we ought not to have targeted, and there is no health in the economy. Former Bank of England Governor Mervyn King, 2016 MONETARY STABILITY IN JAPAN IN THE 1980s The failure of Hayek’s interpretation of the 1920s’ boom and the aftermath to gain widespread acceptance explains why later generations of economists and policymakers returned so enthusiastically to the pursuit of price stability.

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Charles Goodhart of the London School of Economics observed that whenever the Bank of England targeted a particular measure of money supply, that measure’s earlier relationship to inflation broke down. Goodhart’s Law states that any measure used for control is unreliable. The mistake in setting targets lies in assuming that relationships between variables – in this case a certain measure of the money supply and inflation – are stationary. In the real world, human behaviour responds to attempts at control. ‘The essence of Goodhart’s Law,’ write John Kay and Mervyn King in their book Radical Uncertainty, is that ‘any business or government policy which assumed stationarity of social and economic relationships was likely to fail because its implementation would alter the behaviour of those affected and therefore destroy that stationarity.’43 As a former Governor of the Bank of England, Lord King’s prime responsibility had been to implement the 2 per cent inflation target.

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Edward Chancellor * * * THE PRICE OF TIME The Real Story of Interest Atlantic Monthly Press New York Contents List of Illustrations List of Figures Introduction: The Anarchist and the Capitalist PART ONE Of Historical Interest 1 Babylonian Birth 2 Selling Time 3 The Lowering of Interest 4 The Chimera 5 John Bull Cannot Stand Two Per Cent 6 Un Petit Coup de Whisky PART TWO How Low Rates Begot Lower Rates 7 Goodhart’s Law 8 Secular Stagnation 9 The Raven of Basel 10 Unnatural Selection 11 The Promoter’s Profit 12 A Big Fat Ugly Bubble 13 Your Mother Needs to Die 14 Let Them Eat Credit 15 The Price of Anxiety 16 Rusting Money PART THREE The Game of Marbles 17 The Mother and Father of All Evil 18 Financial Repression with Chinese Characteristics Conclusion: The New Road to Serfdom Postscript: The World Turned Upside Down Acknowledgements Select Bibliography Notes Index About the Author Edward Chancellor is the author of Devil Take the Hindmost: A History of Financial Speculation which has been translated into many languages and was a New York Times Book of the Year.

The Tyranny of Metrics
by Jerry Z. Muller
Published 23 Jan 2018

Campbell, holds that “[t]he more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”9 In a variation named for the British economist who formulated it, we have Goodhart’s Law, which states, “Any measure used for control is unreliable.”10 To put it another way, anything that can be measured and rewarded will be gamed. We will see many variations on this theme. Trying to force people to conform their work to preestablished numerical goals tends to stifle innovation and creativity—valuable qualities in most settings.

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It offers numerical information that allows for easy comparison among people and institutions.2 But that simplification may lead to distortion, since making things comparable often means that they are stripped of their context, history, and meaning.3 The result is that the information appears more certain and authoritative than is actually the case: the caveats, the ambiguities, and uncertainties are peeled away, and nothing does more to create the appearance of certain knowledge than expressing it in numerical form.4 Campbell’s Law and Goodhart’s Law are warnings about the inevitable attempts to game the metric when much is at stake. Gaming the metrics takes a variety of forms. Gaming through creaming. This takes place when practitioners find simpler targets or prefer clients with less challenging circumstances, making it easier to reach the metric goal, but excluding cases where success is more difficult to achieve.

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See metric fixation Forbes, 76 Ford Motor Company, 34 foreign aid and philanthropy, 153–56 Freedom of Information Act, 162 From Higher Aims to Hired Hands: The Social Transformation of American Business Schools and the Unfulfilled Promise of Management as a Profession, 12 gaming the metrics, 3, 24–25, 149–50 Geisinger Health System, 108–9, 110–11, 123 General Motors, 33 Geographical Information Systems (GIS), 126 Gibbons, Robert, 55–56 goals: displacement of, through diversion of effort, 169–70; value of short-term over long-term, 20 Goodhart’s Law, 19–20, 24 Google Ngram, 40, 159 Google Scholar, 79 Government Accountability Office, 156 Guardian, The, 163 Halbertal, Moshe, 160, 164 Hayek, Friedrich, 12, 59, 60–61 Healthgrades, 115 Henderson, Rebecca, 150 higher education, 9–14, 175–76; designed to make money, 86–87; encouraging everyone to pursue, 67–68; grading institutions in, 81–86; higher metrics through lower standards in, 69–73; measuring academic productivity, 78–80; pressure to measure performance in, 73–75; raising the number of winners lowering the value of winning with, 68–69; rankings, 75–78; value and limits of rankings in, 81 high-stakes testing, 93 Holmstrom, Bengt, 52, 169 Howard, Philip K., 41 human capital, 72, 98 impact factor measurement, 79 Improving America’s Schools Act, 90 information, distortion of, 23–24 innovation, 20; discouragement of, 140, 150–51, 171–72; employees moving to organizations that encourage, 173; unmeasurable risk for potential benefits of, 61–62 Institute of Medicine, 118–19 intimacy, 160 intrinsic rewards, 53–57, 119–20 Iraq War, 131–34 Johns Hopkins University, 109–10 Johnson, Lyndon, 98 Joint Commission, 115 Joint Stock Companies Act, 30 judgment, 6–7; distrust of, 39–42; measurement demanding, 176–77 “juking the stats,” 2 Kedourie, Elie, 62–63, 73 Kelvin, Lord, 17 Kennedy, Edward, 90 Keystone project, 109–10, 111–12, 176 Khurana, Rakesh, 12 Kilcullen, David, 131–34 Kiplinger, 76 Klarman, Seth, 47 Knight, Frank, 61–62, 151 knowledge: forms of, 59–60; practical, local, 62; pretense of, 60 Kohn, Alfie, 62 Kolberg, William, 90 Kozlowski, Dennis, 144 Lancelot, William, 33 leadership and organizational complexity, 44–47 Lehman Brothers, 146–47 Levy, Steven, 47 Limited Liability Act, 30 litigation, fear of, 42 London Business School, 138–39 Lowe, Robert, 29–30 Lumina Foundation, 67–68, 71 Luttwak, Edward, 35–37 luxury goods, 104 managerialism, 34–37 Manning, Bradley (later Chelsea),162–63 Mass Flourishing: How Grassroots Innovation Created Jobs, Challenge, and Change, 172 Masters of Management, 13 materialist bias, 36 Mayer-Schönberger, Viktor, 35 McNamara, Robert, 34–37, 131 measurement and improvement, 16–17, 101, 107, 111, 119, 123, 132, 176, 183 measuring inputs rather than outcomes, 23–24 “Measuring Progress in Afghanistan,” 132 measuring the most easily measurable, 23 measuring the simple when the desired outcome is complex, 23 Medicaid, 104 Medicare, 104, 114–16, 120–23 medicine: broader picture on metrics, pay-for-performance, rankings, and report cards in, 112–20; case selection bias in, 117–18; Cleveland Clinic, 107–8, 110–11; conclusions from success in, 110–12; cost disease and, 44; discouraging cooperation and common purpose in, 172; financial push to control costs in, 103–4, 119–20; Geisinger Health System, 108–9, 110–11, 123; Keystone project, 109–10, 111–12, 176; measured performance metrics in, 2–5, 107, 123, 176; ranking the American system of, 105–7; reducing readmissions test case, 120–23; rise of metric fixation with increased critique of, 42–43; tales of success in, 107–10 Mercurio, Jed, 2–3 Merton, Robert K., 12, 170 metric fixation, 4–9, 13; in business and finance, 137–51; cost disease and, 44; critique of the professions and apotheosis of choice in, 42–44; defined, 18; distortion of information with, 23–24; distrust of judgment leading to, 39–42; in higher education, 9–14, 67–87, 175–76; innovation and creativity stifled by, 20; key components of, 18; leadership and organizational complexity and, 44–47; lure of electronic spreadsheets in, 47; managerialism and, 34–37; in medicine, 2–5, 42–44, 103–23, 172, 176; by the military, 35–37, 131–35, 176; negative transformations of nature of work with, 19; pay for performance and, 19; in philanthropy and foreign aid, 153–56; in policing, 125–29, 175; predicting and avoiding negative consequences of, 169–73; recurrent flaws in, 23–25; relationship between measurement and improvement in, 17–19; in schools, 11, 24, 89, 175–76; Taylorism and, 31–34; theory of motivation and, 19–20; and transparency as enemy of performance, 159–65 metrics: checklist for when and how to use, 175–83; corruption or goal diversion in gathering and using, 182; costs of acquiring, 180; development of measures for, 181; diagnostic, 92–93, 103, 110, 123, 126, 176; diminishing utility of, 170; gaming the, 3, 23–24, 149–50; kind of information measured by, 177; media depictions of, 1–4; philosophical critiques of, 59–64; purposes of specific measurements and, 178–79; reasons leaders ask for, 180–81; recognition that not all problems are solvable by, 182–83; transactional costs of, 170; used to replace judgment, 6–7; usefulness of information from, 177–78 Michigan Keystone ICU Project, 109–10, 111–12, 176 Middle States Commission on Higher Education, 10–11 Milgrom, Paul, 52, 169 military, American, 35–37, 131–35, 176 Minsky, Hyman, 148 Mintzberg, Henry, 52 Mitchell, Ted, 82 Moneyball, 7 Morieux, Yves, 45, 170 mortgage backed securities, 146–47 motivation: extrinsic and intrinsic rewards and, 53–57, 119–20, 137–38, 144; theory of, 19–20 Muller, Jerry Z., 79 Mylan, 140–42, 143 National Alliance of Business, 90 National Assessment of Educational Progress (NAEP), 91, 97, 99 National Center for Educational Statistics, 97 National Center on Performance Incentives, 95–96 National Health Service, 104, 114, 116–17 National Security Agency, 163 Natsios, Andrew, 155–56 New Public Management, 51–53 Newsweek, 76 No Child Left Behind Act of 2001, 11, 24, 89, 100; problem addressed by, 89–91, 96; Race to the Top after, 94–95; unintended consequences of, 92–94.

The Death of Money: The Coming Collapse of the International Monetary System
by James Rickards
Published 7 Apr 2014

Hayek wrote: The peculiar character of the problem of a rational economic order is determined precisely by the fact that the knowledge of the circumstances of which we must make use never exists in concentrated or integrated form but solely as the dispersed bits of incomplete and frequently contradictory knowledge which all the separate individuals possess. . . . Or, to put it briefly, it is a problem of the utilization of knowledge which is not given to anyone in its totality. Charles Goodhart first articulated Goodhart’s Law in a 1975 paper published by the Reserve Bank of Australia. Goodhart’s Law is frequently paraphrased along the lines, “When a financial indicator becomes the object of policy, it ceases to function as an indicator.” That paraphrase captures the essence of Goodhart’s Law, but the original formulation was even more incisive because it included the phrase “for control purposes.” (In original form, it reads, “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.”)

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Adam Smith The Theory of Moral Sentiments 1759 The “data” from which the economic calculus starts are never for the whole society “given” to a single mind which could work out the implications and can never be so given. Friedrich A. Hayek 1945 Any . . . statistical regularity will tend to collapse once pressure is placed on it for control purposes. Goodhart’s Law 1975 In Shakespeare’s The Merchant of Venice, Salanio asks, “Now, what news on the Rialto?” He’s looking for information, gathering intelligence, and attempting to identify what’s happening in the marketplace. Salanio doesn’t intend to control the business unfolding around him; he knows he cannot.

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The Fed relies on price signals too, particularly those related to inflation, commodity prices, stock prices, unemployment, housing, and many other variables. What happens when you manipulate markets using price signals that are the output of manipulated markets? This is the question posed by Goodhart’s Law. The central planner must suspend belief in one’s own intervention to gather information about the intervention’s effects. But that information is a false signal because it is not the result of free-market activity. This is a recursive function. In plain English, the central planner has no option but to drink his own Kool-Aid.

Broke: How to Survive the Middle Class Crisis
by David Boyle
Published 15 Jan 2014

It all seemed very modern, but was also reminiscent of the great tradition of the Victorian statisticians who tried to measure the morality of children by counting the number of hymns they knew by heart. Part of the problem was that this kind of auditing fell foul of what has become known as Goodhart’s Law: however incompetent staff may be, they will always be smart enough to make targets work for them rather than against them. Take the rule that patients shouldn’t be kept on hospital trolleys for more than four hours. In practice, some hospitals got round this by category shifts: they put them in chairs.

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But journalists had a growing sense that the figures they were given were illusory. Barber himself describes his presentation in a warm room in the Cabinet Office in 2004. When each graph came up, one of the tabloid journalists at the back whispered to himself ‘Bullshit.’ The tragedy of deliverology was that Barber seemed not to have understood the power of Goodhart’s Law. The graphs he sweated over were illusory. Millions of tiny shifts in definition and procedure by every minor civil servant, NHS worker or policeman were making the figures meaningless. It is hardly surprising that the graphs seemed to be going in the right direction. That is what happens when you put intense pressure on the permanent secretary and it filters down the system.

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But we have certainly benefited from the age we lived in, from free university education and student grants, and from inheriting the first staggering rises in house prices from our parents. Some of us trained as professionals in the days when you had some freedom of manoeuvre, before the combination of McKinsey and Goodhart’s Law (see Chapter 7). We don’t talk about money much — we are too middle class for that — and our incomes clearly vary enormously (one of us has even retired in his fifties). But we are not the narrow slice of the class system you might have predicted. We seem actually to straddle a huge variety of different kinds of middle-classness, but we all worry about our children, and their ability to survive in the world that is emerging, here and abroad.

Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones
by James Clear
Published 15 Oct 2018

We care more about getting ten thousand steps than we do about being healthy. We teach for standardized tests instead of emphasizing learning, curiosity, and critical thinking. In short, we optimize for what we measure. When we choose the wrong measurement, we get the wrong behavior. This is sometimes referred to as Goodhart’s Law. Named after the economist Charles Goodhart, the principle states, “When a measure becomes a target, it ceases to be a good measure.” Measurement is only useful when it guides you and adds context to a larger picture, not when it consumes you. Each number is simply one piece of feedback in the overall system.

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I swear I read this line somewhere or perhaps paraphrased it from something similar, but despite my best efforts all of my searches for a source are coming up empty. Maybe I came up with it, but my best guess is it belongs to an unidentified genius instead. “When a measure becomes a target”: This definition of Goodhart’s Law was actually formulated by the British anthropologist Marilyn Strathern. “‘Improving Ratings’: Audit in the British University System,” European Review 5 (1997): 305–321, https://www.cambridge.org/core/journals/european-review/article/improving-ratings-audit-in-the-british-university-system/FC2EE640C0C44E3DB87C29FB666E9AAB.

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See also specific numbered laws four-step process of building a habit 1. cue, 47–48 2. craving, 48 3. response, 48–49 4. reward, 49 habit loop, 49–51 lessons from, 259–64 problem phase and solution phase, 51–53 4th Law of Behavior Change (Make It Satisfying) habit contract, 207–10 habit tracking, 198–99 instant gratification, 188–93 making the cues of bad habits unsatisfying, 205–206 Safeguard soap in Pakistan example, 184–85 Frankl, Victor, 260 Franklin, Benjamin, 196 frequency’s effect on habits, 145–47 friction associated with a behavior, 152–58 garden hose example of reducing, 153 Japanese factory example of eliminating wasted time and effort, 154–55 to prevent unwanted behavior, 157–58 “gateway habit,” 163 genes, 218–21, 226–27 goals effect on happiness, 26 fleeting nature of, 25 shared by winners and losers, 24–25 short-term effects of, 26–27 vs. systems, 23–24 the Goldilocks Rule flow state, 224, 232–33 the Goldilocks Zone, 232 tennis example, 231 good habits creating (table), 96, 136, 178, 212 Two-Minute Rule, 162–67 Goodhart, Charles, 203 Goodhart’s Law, 203 Graham, Paul, 247–48 greylag geese and supernormal stimuli, 102 Guerrouj, Hicham El, 217–18, 225 Guns, Germs, and Steel (Diamond), 149–51 habit contract Bryan Harris weight loss example, 208–209 defined, 208 seat belt law example, 207–208 Thomas Frank alarm example, 210 habit line, 145–47 habit loop, 49–51 habits of avoidance, 191–92 benefits of, 46–47, 239 breaking bad habits (table), 97, 137, 179, 213 in the business world, 265 changing your mind-set about, 130–31 creating good habits (table), 96, 136, 178, 212 downside of, 239–40 effect on the rest of your day, 160, 162 eliminating bad habits, 94–95 as the embodiment of identity, 36–38 formation of, 44–46, 145–47 four-step process of building a habit, 47–53, 259–64 “gateway habit,” 163 identity-based, 31, 39–40 imitation of others’ habits the close, 116–18 the many, 118–21 the powerful, 121–22 importance of, 40–41 outcome-based, 31 and parenting, 267 reframing habits to highlight their benefits, 131–32 short-term and long-term consequences of, 188–90 sticking with, 230–31 suitability for your personality, 221–22 Two-Minute Rule, 162–67 using implementation intention to start, 71–72 Habits Academy, 8 habit shaping, 165–67 Habits Scorecard, 64–66 habit stacking combining temptation bundling with, 110–11 explained, 74–79 habit tracking, 196–200, 202–204 handwashing in Pakistan example of a satisfying behavior change, 184–85 happiness as the absence of desire, 259–60 and goals, 26 relativity of, 263 Harris, Bryan, 208–209 Hebb, Donald, 143 Hebb’s Law, 143 herring gulls and supernormal stimuli, 101–102 hope, 264 Hreha, Jason, 45 Hugo, Victor, 169–70 The Hunchback of Notre Dame (Hugo), 169–70 hyperbolic discounting (time inconsistency), 188–89 identity accepting blanket personal statements as facts, 35 and behavior change, 29–32, 34–36 behavior that is at odds with the self, 32–33 habits as the embodiment of, 36–38, 247–49 identity-based habits, 31, 39–40 letting a single belief define you, 247–49 pride in a particular aspect of one’s identity, 33–34 reinforcing your desired identity by using the Two-Minute Rule, 165 two-step process of changing your identity, 39–40 implementation intention, 69–72 improvements, making small, 231–32, 233, 253 instant gratification, 188–93 Johnson, Magic, 243–44 journaling, 165 Jung, Carl, 62 Kamb, Steve, 117–18 Kubitz, Andrew, 109 Lao Tzu, 249 Tao Te Ching, 249 Latimore, Ed, 132 Lewes, George H., 144 long-term potentiation, 143 Los Angeles Lakers example of reflection and review, 242–44 Luby, Stephen, 183–85 MacMullan, Jackie, 243–44 Martin, Steve, 229–30, 231 Massachusetts General Hospital cafeteria example of environment design change, 81–82 Massimino, Mike, 117 mastery, 240–42 Mate, Gabor, 219 McKeown, Greg, 165 measurements usefulness of, 202–204 visual, 195–96 Mike (Turkish travel guide/ex-smoker), 125–26 Milner, Peter, 105 mind-set shifts from “have to” to “get to,” 130–31 motivation rituals, 132–33 reframing habits to highlight their benefits, 131–32 motion vs. action, 142–43 motivation the Goldilocks Rule, 231–33 maximum motivation, 232 rituals, 132–33 and taking action, 260–61 Murphy, Morgan, 91 negative compounding, 19 Nietzsche, Friedrich, 260 nonconscious activities, 34n nonscale victories, 203–204 novelty, 234 Nuckols, Oswald, 156 observations, 260 obstacles to getting what you want, 152 Olds, James, 105 Olwell, Patty, 93 1 percent changes Career Best Effort program (CBE), 242–44 compounding effect of making changes, 15–16, 17–18 Sorites Paradox, 251–52 operant conditioning, 9–10 opportunities, choosing the right combining your skills to reduce the competition, 225–26 explore/exploit trade-off, 223–25 importance of, 222–23 specialization, 226 outcomes and behavior change, 29–31 outcome-based habits, 31 pain, 206–207 Paper Clip Strategy of visual progress measurements, 195–96 parenting applications of habit strategies, 267 Patterson, John Henry, 171–72 Phelps, Michael, 217–18, 225 photography class example of active practice, 141–42, 144 Plateau of Latent Potential, 21–23 pleasure anticipating vs. experiencing, 106–108 image of, 260 repeating a behavior when it’s a satisfying sensory experience, 184–86, 264 Safeguard soap example, 184–85 Plomin, Robert, 220 Pointing-and-Calling subway safety system, 62–63 positive compounding, 19 The Power of Habit (Duhigg), 9, 47n predictions, making after perceiving cues, 128–29 the human brain as a prediction machine, 60–61 Premack, David, 110 Premack’s Principle, 110 pride manicure example, 33 in a particular aspect of one’s identity, 33–34 priming your environment to make the next action easy, 156–58 problem phase of a habit loop, 51–53 process and behavior change, 30–31 professionals vs. amateurs, 236 progress, 262 proximity’s effect on behavior, 116–18 quitting smoking, 32, 125–26 reading resources Atomic Habits newsletter, 257 business applications of habit strategies, 265 parenting applications of habit strategies, 267 recovering when habits break down, 200–202 reflection and review author’s Annual Review and Integrity Report, 245–46 benefits of, 246–47 Career Best Effort program (CBE) example, 242–44 Chris Rock example, 245 Eliud Kipchoge example, 244–45 flexibility and adaptation, 247–49 importance of, 244–45 Katie Ledecky example, 245 reframing habits to highlight their benefits, 131–32 reinforcement, 191–93 repetition as active practice of a new habit, 144 automaticity, 144–46 to master a habit, 143 photography class example of active practice, 141–42, 144 responding to things based on emotions, 261–62 rewards after sacrifice, 262 immediate vs. delayed, 187–90 purpose of, 49 reinforcement, 191–93 training yourself to delay gratification, 190–93 variable rewards, 235 “wanting” vs.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking
by Michael Bhaskar
Published 2 Nov 2021

In practice, every elite university spends enormous energy securing a good place in the global rankings. In the UK every university and academic must justify their existence with extensive exercises that grade every layer of research and teaching. Universities have thus become classic victims of Goodhart's Law: that when a measure becomes a target, it ceases to be a good measure. This doesn't stop them adding more such statistical measures all the time, and nor does evidence that an over-reliance on them dampens creativity.50 And so the corporate world's audit culture has ballooned into the dominant fact of life for universities, where researchers spend a vanishing portion of their time on their basic job: instead they write proposals for grant money, assess those proposals, sit on committees, write reports, fill in forms.

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utm_source=twitterShare White, Curtis (2003), The Middle Mind: Why Americans Don't Think for Themselves, San Francisco: HarperSanFrancisco White, Curtis (2014), The Science Delusion: Asking the Big Questions in a Culture of Easy Answers, New York: Melville House Whitehead, A.N. (1925), Science and the Modern World, London: Macmillan Williams, Jeffrey J. (2018), ‘The Rise of the Promotional Intellectual’, The Chronicle of Higher Education, accessed 22 August 2018, available at https://www.chronicle.com/article/the-rise-of-the-promotional-intellectual/ Wilson, Edward O. (2017), The Origins of Creativity, London: Allen Lane Winchester, Simon (2008), The Man Who Loved China: The Fantastic Story of the Eccentric Scientist Who Unlocked the Mysteries of the Middle Kingdom, New York: HarperCollins Wolf, Martin (2019), ‘On the Technological Slowdown’, Foreign Affairs, accessed 14 July 2019, available at https://www.foreignaffairs.com/articles/2015-11-19/martin-wolf-innovation-slowdown Wong, May (2017), ‘Scholars say big ideas are getting harder to find’, Phys.org, accessed 10 October 2018, available at https://phys.org/news/2017-09-scholars-big-ideas-harder.html Wootton, David (2015), The Invention of Science: A New History of the Scientific Revolution, London: Allen Lane Wright, Robert (2000), Nonzero: History, Evolution and Human Cooperation, New York: Pantheon Books Wright, Ronald (2006), A Short History of Progress, Edinburgh: Canongate Wu, L., Wang, D., and Evans, J.A. (2019), ‘Large teams develop and small teams disrupt science and technology’, Nature 566, pp. 378–82 Wuchty, Stefan, Jones, Benjamin F., and Uzzi, Brian (2007), ‘The Increasing Dominance of Teams in Production of Knowledge’, Science, Vol. 316 No. 5827, pp. 1036–9 Xinhua (2019), ‘China to build scientific research station on Moon's south pole’, Xinhua, accessed 18 January 2021, available at http://www.xinhuanet.com/english/2019-04/24/c_138004666.htm Yueh, Linda (2018), The Great Economists: How Their Ideas Can Help Us Today, London: Penguin Viking Index ‘0,10’ exhibition 103 ‘0-I’ ideas 31 Aadhaar 265 abstraction 103 AC motor 287, 288 academia 209 Académie des sciences 47 Adam (robot) 235–6 Adams, John 211 Adler, Alfred 188 Adobe 265 Advanced Research Projects Agency (ARPA) 180, 247, 253, 296, 317 AEG 34 aeroplanes 62–6, 68–70, 71, 219 Aeschylus 3 Africa 267, 279–80, 295 age/ageing 122, 158–60, 193 AGI see artificial general intelligence Agrarian Revolution 252 agricultural production 92–3 AI see artificial intelligence Akcigit, Ufuk 193 Alexander the Great 159 Alexander, Albert 52 Alexandrian Library 4, 295, 304 algorithms 175, 185, 196, 224, 235, 245 aliens 240–1, 306, 308–9, 337 Allison, Jim 58 Alphabet 193, 225, 265, 294, 295 AlphaFold software 225–6, 227, 228–9, 233 AlphaGo software 226–7, 228, 233 AlQuraishi, Mohammed 225, 226, 229 Amazon 84–5, 214, 272 Amazon Prime Air 71 American Revolution 139 amino acids 223, 226 Ampère, André-Marie 74–5 Anaximander 35 ancestors 10–12 ancient Greeks 1–6, 7–8, 291, 303–4 Anderson, Kurt 106 Angkor Wat 43 anthrax 47–8, 51 Anthropocene 14–15 anti-reason 211–12 anti-science 211–12 antibacterials 234 antibiotics 38, 52–3, 124, 125, 217, 315 resistance to 235 Apollo missions 70, 315, 316, 317, 318 Apple 33, 85, 159, 185, 186, 193, 272, 296, 312 Aquinas, Thomas 36 AR see augmented reality archaeology 153–4 Archimedes 1–6, 7–8, 19, 27, 32, 37, 39, 291, 304 architecture 103, 115, 188 ARIA 297 Aristarchus 5 Aristotle 24, 108, 282, 304 Arkwright, Richard 25, 26, 34, 253 Armstrong, Louis 103 ARPA see Advanced Research Projects Agency art 99–104, 107–8, 176–7, 236, 321, 339 Artemis (Moon mission) 71, 218 artificial general intelligence (AGI) 226, 237–8, 249, 250, 310, 313, 330, 341 artificial intelligence (AI) 225–9, 233–41, 246–7, 248, 249–52, 262, 266, 300, 310, 312–13, 323, 329, 330, 331, 338 arts 152, 293 see also specific arts Artsimovich, Lev 147 arXiv 116 Asia 264, 267–8, 273, 275 Asimov, Isaac, Foundation 45 Astor, John Jacob 288 astronomy 30, 231, 232 AT&T 85, 181, 183, 185, 197 Ates, Sina T. 193 Athens 24, 295 Atlantis 154 augmented reality (AR) 241–2, 338 authoritarianism 112–13, 284 autonomous vehicles 71, 72, 219 ‘Axial Age’ 108 Azoulay, Pierre 317–18 Bach, J.S. 236 bacillus 46 Bacon, Francis 25, 259 bacteria 38, 46, 53 Bahcall, Safi 31 Ballets Russes 99–100 Baltimore and Ohio railway 67 Banks, Iain M. 310 Bardeen, John 182 BASF 289 Batchelor, Charles 286 Bates, Paul 226 Bayes, Thomas 289 Beagle (ship) 36 Beethoven, Ludwig van 26 Beijing Genomics Institute 257, 294–5 Bell Labs 180–4, 186–8, 190, 206, 214, 217, 289, 296, 322 Benz, Karl 68, 219, 330 Bergson, Henri 109 Bessemer process 80 Bezos, Jeff 71, 326 Bhattacharya, Jay 201, 202, 321 Biden, Joe 59 Big Bang 117, 174, 181 Big Big Ideas 79–80 big ideas 5, 8, 11, 13–19 adoption 28 and an uncertain future 302–36 and art 99–103 artificial 223–38 and the Big Ideas Famine 13 and bisociation 36 blockers to 17–18 and breakthrough problems 46–73, 77, 86, 98, 222, 250, 301 and the ‘burden of knowledge’ effect 154–65, 175, 178, 235, 338 and business formation 95 ceiling 18 conception 37 definition 27–8, 40–1 Enlightenment 132–40, 136–40 era of 109–10 erroneous 176 evidence for 222, 223–54 execution 37 ‘fishing out’ mechanism 152 future of 45, 98, 302–36, 337–43 harmful nature 41–2 how they work 23–45 and the Idea Paradox 178–9, 187, 191, 217, 226, 250, 254, 283–4, 301, 312, 342 and the Kardashev Scale 337–43 long and winding course of 4, 5, 35–8, 136 and the low-hanging fruit paradox 149–54, 167, 178 and luck 38–9 moral 136, 138 nature of 169–72 necessity of 41–3 need for 42–3 normalisation of 171–5, 178 originality of 28 paradox of 143–79 and patents 97 process of 37–8 purchase 37–8 and resources 128 and rights 132–40 and ‘ripeness’ 39 and short-termism 192 slow death of 106–7 slowdown of 98 society's reaction to 216 and specialisation 156, 157–8 today 21–140 tomorrow 141–343 big pharma 31, 60, 185, 217–18, 226 Big Science 118–19 Bill of Rights 137 Bingham, Hiram 153 biology 243–8, 300 synthetic 245–6, 251, 310, 329 BioNTech 218, 298 biotech 195–6, 240, 246, 255–8, 262, 266, 307 bisociation 36 Björk 104 Black, Joseph 26 ‘black swan’ events 307, 310 Bletchley Park 180, 296 Bloom, Nick 91, 92, 93 Boeing 69, 72, 162, 165, 192, 238 Bohr, Niels 104, 118, 159 Boltsmann, Ludwig 188 Boston Consulting Group 204 Botha, P.W. 114 Bowie, David 107 Boyer, Herbert 243 Boyle, Robert 232 Brahe, Tycho 36, 229, 292 brain 166, 246–8, 299–300 collective 299, 300–1 whole brain emulations (‘ems’) 248–9, 341 brain drains 197 brain-to-machine interfaces 247–8 Branson, Richard 71 Brattain, Walter 182 Brazil 266–7, 268, 279 breakthrough organisations 294–9 breakthrough problems 46–73, 77, 86, 98, 222, 234, 250, 301 breakthroughs 2–5, 27–8, 32–7, 41, 129, 152, 156 and expedition novelty 333 hostility to 187 medical 58–60 missing 175 near-misses 160 nuclear power 145 price of 87–98 and short-termism 192 slowdown of 87, 94 society's reaction to 216 and universities 204 see also ‘Eureka’ moments breast cancer 94 Brexit referendum 2016: 208 Brin, Sergey 319, 326 Britain 24, 146, 259, 283, 297 see also United Kingdom British Telecom 196 Brunel, Isambard Kingdom 67 Brunelleschi 232 Bruno, Giordano 216 Buddhism 108, 175, 264–5, 340 Buhler, Charlotte 188–9 Buhler, Karl 188–9 ‘burden of knowledge’ effect 154–65, 175, 178, 235, 338 bureaucracy 198–87, 280–1 Bush, George W. 211 Bush, Vannevar 168, 314–15, 317 business start-ups 95–6 Cage, John 104 Callard, Agnes 111 Caltech 184 Cambridge University 75, 76, 124, 235–6, 257, 294–6 canals 67 cancer 57–61, 76, 93–4, 131, 234, 245, 318 research 59–61 capital and economic growth 88 gray 192, 196 human 275, 277 capitalism 36, 111–13, 186, 189, 191–8 CAR-Ts see chimeric antigen receptor T-cells carbon dioxide emissions 220–1 Cardwell's Law 283 Carey, Nessa 244 Carnap, Rudolf 189 Carnarvon, Lord 153 cars 289 electric 71 flying 71 Carter, Howard 153 Carter, Jimmy 58 Carthage 3, 43 Cartright, Mary 163 CASP see Critical Assessment of Protein Structure Prediction Cassin, René 135 Catholic Church 206, 230 Cavendish Laboratory 76, 294 Cell (journal) 234 censorship 210–11 Census Bureau (US) 78 Centers for Disease Control 212 Cerf, Vint 253 CERN 118, 233, 239, 252, 296 Chain, Ernst 52, 60, 124 Champollion, Jean-François 155 Chang, Peng Chun 135 change 10–13, 18–19, 24 rapid 30, 32 resistance to 222 slowdown 85 chaos theory 163 Chaplin, Charlie 104 Chardin, Pierre Teilhard de 300 Charpentier, Emmanuelle 244, 256 chemistry 49, 56, 104, 117, 118, 124, 149–50, 159, 241, 244 chemotherapy 57 Chicago 10 chicken cholera 46 chimeric antigen receptor T-cells (CAR-Ts) 58, 61 China 15, 25, 71–2, 111, 112, 138, 208, 213, 216, 255–64, 265, 266, 267, 268, 275, 277, 279, 280, 283, 284–5, 312, 313, 314, 319, 328 Han 259, 260 Ming 284, 308, 309 Qing 260 Song 24, 259–60, 306 Tang 259–60 Zhou 259 Christianity 108, 303–4, 340 Church, George 245 cities 270–2, 308–9, 340 civilisation collapse 42–4 decay 187 cleantech 195 climate change 219–21, 284, 313–14, 338 clinical trials 218 cliodynamics 339 coal 23, 24, 26, 80, 220 Cocteau, Jean 101 cognitive complexity, high 332–3 cognitive diversity 281–3 Cognitive Revolution 252 Cohen, Stanley N. 243, 244 collective intelligence 339 collectivism 282 Collison, Patrick 117, 272 colour 75 Coltrane, John 104 Columbian Exchange 177 Columbus 38 comfort zones, stepping outside of 334 communism 111, 133, 134, 173, 217, 284 companies creation 95–6 numbers 96–7 competition 87, 283 complacency 221–2 complexity 161–7, 178, 204, 208, 298, 302, 329 high cognitive 332–3 compliance 205–6 computational power 128–9, 168, 234, 250 computer games 107 computers 166–7, 240, 253 computing 254 see also quantum computing Confucianism 133, 259 Confucius 24, 108, 109, 282 Congressional Budget Office 82 connectivity 272 Conon of Samos 4 consciousness 248, 340 consequences 328–9 consolidation, age of 86 Constantine 303 convergence 174, 311–12 Copernicus 29, 30, 41, 152, 171, 229, 232, 292 copyright 195 corporations 204–5 cosmic background microwave radiation 117, 181 cotton weaving, flying shuttle 24–5 Coulomb, Charles-Augustin de 74–5 counterculture 106 Covid-19 (coronavirus) pandemic 13, 14, 15, 55, 86, 113–14, 193, 202, 208, 212, 218, 251–2, 263, 283–4, 297–8, 309, 318, 327 vaccine 125, 245 Cowen, Tyler 13, 82, 94–5, 221 cowpox 47 creativity 188, 283 and artificial intelligence 236 crisis in 108 decrease 106–8 and universities 203 Crete 43 Crick, Francis 119, 296 CRISPR 243, 244, 251, 255–8, 299 Critical Assessment of Protein Structure Prediction (CASP) 224–6, 228 Cronin, Lee 242 crop yields 92–3 cultural diversity 281–3 cultural homogenisation 177 cultural rebellion 106–7 Cultural Revolution 114, 305 culture, stuck 106 Cunard 67 Curie, Marie 104, 144, 203, 289–90, 332 Daniels, John T. 62–3 Daoism 259 dark matter/energy/force 338 DARPA see Defense Advanced Research Projects Agency Darwin, Charles 34, 35–6, 37–8, 41, 77, 109, 118, 171, 289 Darwin, Erasmus 35 data 233 datasets, large 28 Davy, Sir Humphrey 149, 150 Debussy, Claude 100–1 decision-making, bad 43–4 Declaration of Independence 1776: 137 Declaration of the Rights of Man and Citizen 1789: 137 DeepMind 225–9, 296 Defense Advanced Research Projects Agency (DARPA) 315 democracy 111–12 Deng Xiaoping 261 deoxyribonucleic acid (DNA) 119, 223–4, 243, 251, 255, 339 DNA sequencing 56 Derrida, Jacques 109 Deutsch, David 126, 203 Diaghilev, Sergei 99–101 Diamond, Jared 42 Digital Age 180 digital technology 241–2, 243 diminishing returns 87, 91, 94, 97, 118, 123, 126, 130–1, 150, 161, 169, 173, 222, 250, 276, 285, 301 Dirac, Paul 159–60 disruption 34, 96, 109, 119, 157 diversity, cultural 281–3 DNA see deoxyribonucleic acid Dorling, Danny 171 Doudna, Jennifer 244, 251, 256 Douglas, Mary 290 Douthat, Ross 14, 106 drag 65 Drake equation 306 Drezner, Daniel 214 drones, delivery 71, 72 Drucker, Peter 189 drugs 55–7, 124, 235 Eroom's Law 55, 57, 61, 92–3, 119, 161, 234, 245, 338 and machine learning 234 research and development 55–7, 61, 92–4, 119, 161, 172–3, 217–18, 234, 245, 315, 338 see also pharmaceutical industry Duchamp, Marcel 103, 171 DuPont 184 Dutch East India Company 34 Dyson, Freeman 120 dystopias 305–8 East India Company 34 Easter Island 42–3 Eastern Europe 138 ecocides 42–3 economic growth 240, 272, 273, 316 endogenous 94 and ideas 88, 89–92, 95 process of 87–8 slowdown 82, 83, 84, 85, 178 economics 87–9, 98, 339, 340 contradictions of 87 Economist, The (magazine) 188 Edelman annual trust barometer 209 Edison, Thomas 183–4, 286–9, 290, 293 education 127, 277, 324–8 Einstein, Albert 11, 29, 74, 77, 104, 109, 117, 119, 124, 159–60, 203, 332 Eisenstein, Elizabeth 231 Eldredge, Niles 30 electric cars 71 electricity 11, 74–7, 81, 286–7, 289 electromagnetic fields 76 electromagnetic waves 75, 76 elements (chemical) 149–50 Elizabeth II 144–5 employment 204–5 Encyclopædia Britannica 97, 128, 155 ‘End of History’ 112 energy 337–8, 341–2 availability 85 use per capita 85 see also nuclear power engineering 243 England 25, 144–5, 309 Englert, François 118 Enlightenment 130, 136–40, 252 see also Industrial Enlightenment; neo-Enlightenment Eno, Brian 295 entrepreneurship, decline 96 epigenetics 164 epigraphy 236–7 epistemic polarisation 210 Epstein, David 334 Eratosthenes 5 Eroom's Law 55, 57, 61, 92–3, 119, 161, 234, 245, 338 ethical issues 256–7 Euclid 3, 304 ‘Eureka’ moments 2–5, 35, 36–7, 129, 163 Europe 95, 247, 258–60, 268, 268, 271, 283, 304, 308 European Space Agency 71 European Union (EU) 206, 216, 262, 266 Evans, Arthur 153 evolutionary theory 30, 35–6 expedition novelty 333 experimental spaces 296–8 Expressionism 104 Facebook 34, 159, 170, 197 Fahrenheit 232 failure, fear of 335 Faraday, Michael 75 FCC see Future Circular Collider FDA see Food and Drug Administration Federal Reserve (US) 82 Feigenbaum, Mitchell 163 fermentation 49 Fermi, Enrico 143, 159, 306 Fermi Paradox 306 Fernández-Armesto, Felipe 109 fertility rates 269 Feynman, Richard 77, 166, 332 film 104, 106–7, 108, 115 financialism 191–8, 206–7, 214, 217, 219 Firebird, The (ballet) 99–100 ‘first knowledge economy’ 25–6 First World War 54, 99, 104, 187, 188–9 Fisk, James 182 Fleming, Alexander 38, 52, 60, 332 flight 36, 62–6, 68–70, 71, 335 Flint & Company 64 flooding 220, 284 Florey, Howard 52, 60, 124, 332 Flyer, the 62–4, 66, 72 Foldit software 225 Food and Drug Administration (FDA) 55, 60, 93, 212 food supply 81 Ford 34, 253 Ford, Henry 68, 104, 219 Fordism 81 Foucault, Michel 110 Fraenkel, Eduard 124 France 49–51, 54, 64, 67, 95, 279, 309, 332 franchises 31 Franklin, Benjamin 119, 211 Frederick the Great 292 French Revolution 137, 275 Freud, Sigmund 34, 36, 77, 104, 171, 188, 190, 216 frontier 278–9, 283–4, 302, 310–11 Fukuyama, Francis 111–12 fundamentalism 213 Future Circular Collider (FCC) 239 futurology 44 Gagarin, Yuri 70 Galen 303 Galileo 206, 231, 232, 291, 322 Galois, Évariste 159 GDPR see General Data Protection Regulation Gell-Mann, Murray 77 gene editing 243–4, 251, 255–8 General Data Protection Regulation (GDPR) 206 General Electric (GE) 33, 184, 265, 288, 333 General Motors 289 Generation Z 86 genes 223–4 genetic engineering 243–4, 251, 253, 255–8 genetic science 163–4, 202 genius 26 genome, human 119, 202, 244, 255–7, 296, 313 genome sequencing 243–4 germ theory of disease 50–1, 53 Germany 54, 95, 96, 279, 283, 292, 332 Gesamtkunstwerk 99 Gibson, William 241 Glendon, Mary Ann 135 global warming 147 globalisation 177 Go 226–7 Gödel, Kurt 41, 168 Goldman Sachs 197 Goodhart's Law 199 Google 34, 85, 185, 197, 240, 272, 318 20 per cent time 319–20 Google Glass 241 Google Maps 86 Google Scholar 116 Google X 294 Gordon, Robert 13, 83, 94–5 Gouges, Olympe de 137 Gould, Stephen Jay 30 Gove, Michael 208 government 205, 207, 214, 216, 252, 267–8, 297 funding 185–6, 249, 252, 314–19, 321 GPT language prediction 234, 236 Graeber, David 13–14, 111 grants 120, 185–6, 195, 202, 316, 317, 319, 321–3 gravitational waves 117–18, 119 Great Acceleration 309–10 Great Convergence 255–301, 339 Great Disruption 96 Great Enrichment (Great Divergence) 23, 26, 258 Great Exhibition 1851: 293, 309 Great Stagnation Debate 13–14, 16, 17, 45, 72, 82–3, 87, 94–6, 129, 150, 240, 279, 338 Greenland 42 Gropius, Walter 103 Gross Domestic Product (GDP) 82, 90, 128, 278, 318 GDP per capita 23, 78, 82 growth cultures 25 growth theory, endogenous 88–9, 94 Gutenberg, Johannes 36 Guzey, Alexey 200, 322 Haber, Fritz 332 Haber-Bosch process 289 Hadid, Zaha 152 Hahn, Otto 144 Hamilton, Margaret 316 Harari, Yuval Noah 114–15, 236 Harris, Robert 307 Harvard Fellows 200 Harvard, John 156 Harvey, William 34, 291–2 Hassabis, Demis 229, 233 Hayek, Friedrich 189 Hegel, Georg Wilhelm Friedrich 36 Heisenberg, Werner 41, 159, 168, 332 heliocentric theory 5, 29, 118, 232, 304 helium 145 Hendrix, Jimi 105 Henry Adams curve 85 Hero of Alexandria 39 Herper, Matthew 55 Hertz, Heinrich 76 Herzl, Theodor 188 Hesse, Herman 307 Hieron II, king of Syracuse 1–2 Higgs, Peter 118 Higgs boson 117–18, 119, 239 Hinduism 133 Hiroshima 144 Hitler, Adolf 138, 188 Hodgkin, Dorothy 124, 332 Hollingsworth, J.

Brave New Work: Are You Ready to Reinvent Your Organization?
by Aaron Dignan
Published 1 Feb 2019

If someone who onboards employees truly delivers “members who are informed, connected, and ready to contribute,” do we really need to specify the how? Steering Metrics. Legacy Organizations are obsessed with measurement, often using it as a form of control—to find and punish weak performance. But when we obsess over metrics, we fall victim to Goodhart’s law, which states that a measure that becomes a target ceases to be a good measure. Why? Because human beings will manipulate the situation in order to move the numbers. Instead, we should think of metrics as guides for steering toward our purpose. If we make an app that has a purpose of helping people lose weight, then average time in app is interesting, but only insofar as playing with the app translates to healthier users.

…

The basic idea is that each person in the organization should identify their strategic objectives for the quarter and break those down into the more measurable key results that will indicate if they’ve been successful. It’s goal setting with a modern twist. OKRs should be stretch goals, not easily accomplished (to prevent sandbagging), and transparent (to encourage collaboration and understanding). There are two things to watch out for here. The first is, again, Goodhart’s law. Once people set their OKRs, they’re going to do everything they can to hit them, including things that aren’t good for the business. As W. Edwards Deming observed, “People with targets and jobs dependent upon meeting them will probably meet the targets, even if they have to destroy the enterprise to do it.”

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Buckminster, 247 functional division, 112 functions vs. integration, 79–80 Gantt, Henry, 24 GDP, 27 GE Appliances, 76 Getz, Isaac, 238–39 Gibson, William, 243 gig economy, 169 Gilbreth, Frank and Lillian, 25 Git, 132–33 GitLab, 120, 133 Gladwell, Malcolm, 216 Glassdoor, 170 Gloger, Boris, 199 Godin, Seth, 180 Goodhart’s law, 60, 87 Google, 87, 107, 135–36, 235, 252, 268 G Suite, 135 Project Aristotle, 221 Gordon, Deborah, 106 Gore, Bill, 142 Gore, W. L. and Associates, 16, 69–70, 79, 142 Gorilla Glass, 103 Gould, Stephen Jay, 103 governance meetings, 122 governing constraints, 46 Gower, Bob, 222 Graham, Benjamin, 30 Graham, Paul, 230 Grant, Adam, 142 gratitude, 148 Gray, Dave, 196 G Suite, 135 Haier, 76, 80 Hamel, Gary, 26 Hammond, Robert, 188 Handelsbanken, 13, 94, 227–28 Hansson, David Heinemeier, 68–69 Harrison, Scott, 224–25 Hawk, Tony, 259 healthcare industry, 34–35 Buurtzorg in, 13, 34–36, 38, 79, 105, 144, 218 Heath, Chip and Dan, 212 Heppner, Frank, 46 Herzberg, Frederick, 165 hierarchies, 77–78, 258 High Line, 188 Hillaker, Harry, 88 Hillman, James, 36 hiring, 79, 142–43 Hoffman, Reid, 88 Holacracy, 71, 122, 202 HolacracyOne, 89 Human Side of Enterprise, The (McGregor), 39–41 Husney, Jordan, 89 Huxley, Aldous, 22 hygiene factors, 165, 173 ICBD (Intentions, Concerns, Borders, and Dreams), 222–23 IDEO, 142–43 incentive compensation, 171–72 Indie.vc, 253–54 influence, 78 information, 14, 54, 127–37 information symmetry, 130, 134, 170, 190 initial public offerings (IPOs), 254, 255 Innosight, 29 innovation, 14, 54, 102–9, 188 innovator’s dilemma, 91 integration vs. functions, 79–80 Integrative Decision Making (IDM), 71–73 internet, 84, 131 Intrinsic Motivation (Deci), 42 investment, 251–55 James, LeBron, 143, 172 Jamieson, Alex, 222 Jobs, Steve, 103 job satisfaction, 165 Johnson, Steven, 189 Joint Special Operations Command (JSOC), 128–29, 130 Kahneman, Daniel, 165 Kanigel, Robert, 22 Kegan, Robert, 152–53 knee-jerk reactions, 28 Kotter, John, 186 KPMG, 32 Kroger, 59 Kroghrud, Ivar, 147 labor productivity growth, 33–34 Lahey, Lisa, 152–53 Laloux, Frederic, 105 language, 217 Lasseter, John, 191 lattice organizations, 142 leader, role of, 223–28 leadership gap, 166 Leading Change (Kotter), 186 Lean Change Management (Little), 201 Lean Startup method, 107–8 learning, 152, 153, 156–57, 160, 162, 200 by doing, 230–31 faster, 88 games and activities for, 200 retrospectives and, 123–24 validated, 108 Legacy Organizations, 5, 21, 38, 47, 59, 237, 258 authority in, 66 decision making in, 69 information in, 129, 131 measurement in, 60 membership in, 140 operating systems of, 12 performance targets and, 97 strategy in, 86 liminal space, 196, 197, 201 Little, Jason, 201 Little Book of Beyond Budgeting, The (Morlidge), 96–97 locus of control, 154, 155 Long-Term Stock Exchange (LTSE), 254–55 looping, 193, 201–16, 229, 236 and conducting experiments, 213–16 and proposing practices, 207–12 and sensing tensions, 202–6 Lyft, 169 Machiavelli, Niccolò, 248 Made to Stick (Heath and Heath), 212 management, 26–27, 81 innovations in, 20 open-book, 130 org charts for, 7–9, 24, 77, 78, 81, 114, 189 market pay, 167–68 Marquet, David, 67 Maslow, Abraham, 38 Masters of Scale, 88 mastery, 14, 54, 151–62 maturity, 154, 155–56, 255–56 McChrystal, Stanley, 128–29, 130, 197 McGregor, Douglas, 39–41, 158 McKeown, Greg, 62 McKinsey, James O., 24–25, 95 McKinsey & Company, 24, 32, 143, 187 Medium, 84–85, 86 meetings, 3–4, 6, 119 moratorium on, 123 in OS Canvas, 14, 54, 118–26 structures in, 124–25 membership, 14, 54, 138–50 mergers and acquisitions (M&A), 32, 33 metrics, 60–61 Meyer, Erin, 258 Microsoft, 170 microwave, 103 Mindset (Dweck), 154 mindsets: fixed and growth, 154–55 see also Complexity Conscious mindset; People Positive mindset minimum viable policy, 68–69 Mitchell, Melanie, 129 Mitra, Sugata, 257 Morlidge, Steve, 96–97 Morning Star Company, 13, 55–56, 99, 168 motivation, 41–42, 64, 74, 165, 173 multiplayer software, 135 Münsterberg, Hugo, 25 murmuration, 194 Netflix, 113, 167–68, 219 Netscape, 59 networks, dynamic, 77–78 New York Summit, 147 New York Times, 147, 165 Nietzsche, Friedrich, 179 noncompete clauses, 144 Office of Strategic Services, U.S., 6–7 Ohno, Taiichi, 20, 111 OKR (objectives and key results), 87–88 Oktogonen Foundation, 94 Olympic basketball team, U.S., 172 one-on-ones, 121–22 OODA loop, 88, 90 Operating Manual for Spaceship Earth (Fuller), 247 operating system, organizational (OS), 12–13, 17, 18, 43, 215 agility and, 19 changing, see change economic, 246–47, 248 evolutionary, see Evolutionary Organizations management innovations and, 20 Operating System Canvas (OS Canvas), 14, 53–57 authority, 14, 54, 63, 65–74 compensation, 14, 54, 163–73 how to use, 174, 270–72 information, 14, 54, 127–37 innovation, 14, 54, 102–9, 188 mastery, 14, 54, 151–62 meetings, 14, 54, 118–26 membership, 14, 54, 138–50 purpose, 14, 54, 68–64, 67, 85 resources, 14, 54, 93–101 strategy, 14, 54, 83–92 structure, 14, 54, 75–82, 111 workflow, 14, 54, 110–17 operating systems, 9 for traffic flow, see traffic flow organizational debt, 27–29, 91 organizations, 255 agility in, 19, 20, 28–29 as complex systems, 45, 187–88 cooperatives, 250 decentralized autonomous, 250–51 entry/exit rates of, 33 evolutionary, see Evolutionary Organizations governance of, 122 investment and, 251–55 lattice, 142 legacy, see Legacy Organizations longevity of, 29–30 mergers and acquisitions, 32, 33 new forms of incorporation, 248–51, 252 operating systems of, see operating system, organizational org charts, 7–9, 24, 77, 78, 81, 114, 189 return on assets of, 31, 32 as set of membranes, 139–40 three structures of, 78 OS Canvas, see Operating System Canvas Ostrom, Elinor, 98 over statements, 89 Page, Larry, 136 Patagonia, 85, 130–31, 133, 249, 259 pay, see compensation People Positive mindset, 13, 36–43, 53, 55–57, 190, 195, 199, 244, 258–59, 267 authority and, 74 compensation and, 173 information and, 137 innovation and, 109 mastery and, 162 meetings and, 126 membership and, 150 purpose and, 64 resources and, 101 strategy and, 92 structure and, 82 workflow and, 117 Percolate, 131–32 performance, 46 individual, 158–60, 172 Petrarch, 224 Pflaeging, Niels, 78, 180, 189–90 Pixar, 119–20, 191–92 planning, 91, 95, 96, 100 see also strategy Plato, 3 Play-Doh, 103 polycentric governance, 98 PopSugar, 135 practices, proposing, 207–12 priming, 193, 197–201, 236 Principles (Dalio), 152 Principles of Scientific Management, The (Taylor), 23–24, 29 priorities, 88–89 profit, 59–60 Project Aristotle, 221 projects, 113, 114, 117 management of, 112, 237 sprints and, 115, 237–38 status of, 121, 132 work in progress and, 115–16, 132 proposing practices, 207–12 psychological safety, 219–23, 236 purpose, 14, 54, 68–64, 67, 85 push vs. pull, 131–32 Quaroni, Guido, 192 railroads, 8, 22–23 Raworth, Kate, 246–47 Ready, The, 17–19, 123, 125, 143, 149, 174, 190, 217 recruiting and hiring, 79, 142–43 Reddit, 135 red team, 90–91 REI, 85 Reinventing Organizations (Laloux), 105 relatedness, 42 relief, 236–37 reputation, 78 resistance, 233–34 resources, 14, 54, 93–101 retrospectives, 123–24 return on assets (ROA), 31, 32 Rework (Fried and Hansson), 68–69 Ries, Eric, 107–8, 254, 255 risk, 68, 122, 132, 231 barbell strategy and, 86–87, 105–6 ritual, 143 Robertson, Brian, 202 Rogers, Carl, 38 roles, 72, 77, 80, 81, 111, 141, 157 decision making and, 72, 73 mixing of, 157–58 Rotter, Julian B., 154 roundabouts, 10–12, 13, 47, 55 Ruimin, Zhang, 76 Russell, Bertrand, 247 Ryan, Richard, 42 sabotage, 5–7 safety, psychological, 219–23, 236 Sahlberg, Pasi, 12 Saint-Exupéry, Antoine de, 212 salary, 164, 165, 168 see also compensation Salary.com, 170 Salesforce, 119 S&P 500, 29–30, 60 Santa Fe, USS, 67 Santa Fe Institute, 29 scaling change, 234–39 scenario planning, 90 Schaar, Tom, 259 Scientific Management, 22–24, 26, 48 Scott, Kim, 120 scribes, 122–23 Securities and Exchange Commission (SEC), 104, 255 self-determination theory, 42 self-employment, 33 self-evaluation, 154 self-management, 16–17 self-set pay, 168 Semler, Ricardo, 245, 258 Seneca, 189 Senge, Peter, 153, 202 sensing, 202–6, 231–32 signal-controlled intersections, 9–12, 13, 46, 55 Simple Sabotage Field Manual, 7 Sinek, Simon, 222 Sisodia, Raj, 60 Slack, 119, 134, 135 SLAM teams, 80 Snowden, Dave, 156, 188–89 Sociocracy, 70–71, 122 space: creating, 224–26, 228 holding, 226–28 liminal, 196, 197, 201 Spencer, Percy, 103 Spotify, 112–13, 160, 218 spread, 217–18 sprints, 115, 237–38 standards vs. defaults, 106–7 Starbucks, 85 startups, 27–28, 33, 76–77, 107, 197, 254 Lean Startup method, 107–8 status quo, 48, 90–91, 233 steering metrics, 60–61 stocks, 30–31 strategy, 14, 54, 83–92 strategy+business, 76 strategy review meetings, 3–4 structure, 14, 54, 75–82, 111 Svenska Handelsbanken, 13, 94, 227–28 Taleb, Nassim Nicholas, 86–88, 106 targets, 95, 97, 101 Taylor, Frederick Winslow, 21–24, 26, 29, 111, 153, 186, 257 teams, 79, 82, 113, 117, 141, 142, 172, 225–26 Ballpoint game for, 199–200 charters for, 144–45 dynamic, 81 gratitude and, 148 ICBD technique for, 222–23 red, 90–91 retrospectives and, 123–24 rituals and, 143 SLAM, 80 sprints and, 115, 237–38 status updates and, 121 teams of, 77, 197 work in progress and, 115–16, 132 technology, 256–57 TED, 128, 246, 257 telephone, 103 Teller, Astro, 49 tensions, sensing, 202–6 Tesla, Inc., 62, 86 Theory X and Theory Y, 39–41, 130 Thomison, Tom, 89 tipping point, 216 Torvalds, Linus, 132 Toyota, 20, 111, 235 TPG, 253 traffic flow, 9–12, 45 roundabouts for, 10–12, 13, 47, 55 signal-controlled intersections for, 9–12, 13, 46, 55 tragedy of the commons, 98 training, 6, 156–57 transparency, 129, 130–31, 134, 136, 190, 195, 258 compensation and, 168, 169–71, 173 radical, 152, 154 trust, 236 twenty percent time, 107 Twitter, 84 Urwick, Lyndall, 25 User Manual to Me, 147–48 value creation, 78, 111–14, 160 Valve Software, 66, 107 Vang-Jensen, Frank, 227 Vanguard, 48 venture capital, 253 Vrba, Elisabeth, 103 VUCA, 43 wages, 34, 166 see also compensation Wallander, Jan, 94, 227 Warby Parker, 96 waterline principle, 69–70, 72 Weber, Max, 25 WeWork, 87 Whole Foods, 59, 61, 170, 259 Wikipedia, 140 Williams, Ev, 84–85 workflow, 14, 54, 110–17 working in public, 132 work in progress (WIP), 115–16, 132 World War II, 6–7 Wright, Orville and Wilbur, 103 Y Combinator, 230 Zanini, Michele, 26 Zappos, 144 al-Zarqawi, Abu Musab, 129 Zobrist, Jean-François, 37, 42–43 Zuckerberg, Mark, 88 ABCDEFGHIJKLMNOPQRSTUVWXYZ About the Author Aaron Dignan is the founder of The Ready, an organization design and transformation firm that helps institutions like Johnson & Johnson, Charles Schwab, Kaplan, Microsoft, Lloyds Bank, Citibank, Edelman, Airbnb, Cooper Hewitt Smithsonian Design Museum, and charity: water change the way they work.

Obliquity: Why Our Goals Are Best Achieved Indirectly
by John Kay
Published 30 Apr 2010

Perhaps the target had undesirable effects on behavior—dispatchers prioritized the calls they knew could be answered quickly at the expense of cases that were more difficult.) All we do know is that the introduction of measurement and control distorted the information needed to implement that measurement and control. This phenomenon is known as Goodhart’s law,6 after the British economist who observed that as soon as governments adopted monetary targets the aggregates they targeted changed their meaning and significance. The story of the Soviet nail factory whose output target was based on the weight of nails it produced and was achieved by manufacturing a single giant nail is probably apocryphal.

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forest fires Fortune Foundations of Economic Analysis (Samuelson) Founding Fathers “foxes” France Franklin, Benjamin Franklin’s gambit Franklin’s rule free markets French Revolution Fuld, Dick Gallipoli expedition games Gardner, Chris Gardner, Helen Gare d’Orsay Gates, Bill Gaziano, Joseph S. General Electric (GE) Generally Accepted Accounting Principles (GAAP) genetics Germany Getty, John Paul “Getty kouros” Gilmartin, Ray Gladwell, Malcolm Glaxo God Goldman Sachs gold standard Golf Is Not a Game of Perfect (Rotella) Gombrich, Ernst Goodhart’s law Google government graphic design Great Britain Great Society greed Green, Hetty gross domestic product (GDP) Guinness World Records Hahn, Frank halo effects Hammer, Michael Hanson Trust Harvard Business School Hayek, Friedrich von health care hedge funds “hedgehogs” Heights of Abraham Heller, Robert heroin addicts hindsight historical analysis Hitler, Adolf Hobbes, Thomas Honda Hopkins, Gerard Manley How the Mighty Fall (Collins) human development index (HDI) Hume, David Huxley, Aldous hypertension IBM Iceland ICI imagination, failure of IMD immunization income levels “incompletely theorized agreement” incremental change India industrialization infinite series information technology insider trading intelligence reports intermediate goals International Financial Reporting Standards (IFRS) intuition iPods Iraq war Irish potato famine iteration Jacobs, Jane Japan Jencks, Charles Jenkins, Roy Jobs, Steve job security Johnson, Lyndon B.

Human Compatible: Artificial Intelligence and the Problem of Control
by Stuart Russell
Published 7 Oct 2019

Visiting an ailing friend in hospital will, under such a system, have no more moral significance and emotional value than stopping at a red light. Second, the scheme falls victim to the same failure mode as the standard model of AI, in that it assumes that the stated objective is in fact the true, underlying objective. Inevitably, Goodhart’s law will take over, whereby individuals optimize the official measure of outward behavior, just as universities have learned to optimize the “objective” measures of “quality” used by university ranking systems instead of improving their real (but unmeasured) quality.8 Finally, the imposition of a uniform measure of behavioral virtue misses the point that a successful society may comprise a wide variety of individuals, each contributing in their own way.

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For a low-tech version of human susceptibility to misinformation, in which an unsuspecting individual becomes convinced that the world is being destroyed by meteor strikes, see Derren Brown: Apocalypse, “Part One,” directed by Simon Dinsell, 2012, youtube.com/watch?v=o_CUrMJOxqs. 7. An economic analysis of reputation systems and their corruption is given by Steven Tadelis, “Reputation and feedback systems in online platform markets,” Annual Review of Economics 8 (2016): 321–40. 8. Goodhart’s law: “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” For example, there may once have been a correlation between faculty quality and faculty salary, so the US News & World Report college rankings measure faculty quality by faculty salaries.

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See also assistance games Gates, Bill, 56, 153 GDPR (General Data Protection Regulation), 127–29 Geminoid DK (robot), 125 General Data Protection Regulation (GDPR), 127–29 general-purpose artificial intelligence, 46–48, 100, 136 geometric objects, 33 Glamour, 129 Global Learning XPRIZE competition, 70 Go, 6, 46–47, 49–50, 51, 55, 56 combinatorial complexity and, 259–61 propositional logic and, 269 supervised learning algorithm and, 286–87 thinking, learning from, 293–95 goals, 41–42, 48–53, 136–42, 165–69 God and Golem (Wiener), 137–38 Gödel, Kurt, 51, 52 Goethe, Johann Wolfgang von, 137 Good, I. J., 142–43, 153, 208–9 Goodhart’s law, 77 Goodman, Nelson, 85 Good Old-Fashioned AI (GOFAI), 271 Google, 108, 112–13 DeepMind (See DeepMind) Home, 64–65 misclassifying people as gorillas in Google Photo, 60 tensor processing units (TPUs), 35 gorilla problem, 132–36 governance of AI, 249–53 governmental reward and punishment systems, 106–7 Great Decoupling, 117 greed (as an instrumental goal), 140–42 Grice, H.

The Rise and Fall of Nations: Forces of Change in the Post-Crisis World
by Ruchir Sharma
Published 5 Jun 2016

That check can be pretty reliable, except that in 2015 reports emerged that government authorities were instructing developers to keep the lights on even in empty apartment complexes. The aim was to drive up electricity consumption data so that it would confirm official economic growth claims. This is a classic case of Goodhart’s Law, which says that once a measure becomes a target, it ceases to be useful, partly because so many people have an incentive to doctor numbers to meet it.6 One useful and timely data source is the prices in global financial markets, which in normal times will accurately capture the world’s best collective guess about the likely prospects of an economy.

…

The World Bank also puts out rankings of countries for everything from quality of roads to how many days it takes to open a business, and these rankings have become very popular. That creates a problem, as more than a few countries have started hiring consultants to help them raise their rankings (another example of Goodhart’s Law in action). In 2012 President Vladimir Putin set a goal of raising Russia’s rank for “ease of doing business” from 120 to top 20 within six years, and he soon saw results. By 2015, Russia was ranked at 51—more than thirty places ahead of China, and sixty places ahead of Brazil and India. That raised a question: If it was so easy to do business in Russia, why wasn’t anyone doing business there?

…

EPWP1401, February 2014. 4 Ghada Fayad and Roberto Perrelli, “Growth Surprises and Synchronized Slowdowns in Emerging Markets: An Empirical Investigation,” International Monetary Fund, 2014. 5 Lant Pritchett and Lawrence Summers, “Asiaphoria Meets Regression to the Mean,” National Bureau of Economic Research, Working Paper no. 20573, October 2014. 6 “Goodhart’s Law,” BusinessDictionary.com. 7 James Surowiecki, The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies, and Nations (New York: Doubleday, 2004). 8 Ned Davis, Ned’s Insights, November 14, 2014. 9 “Picking Apart the Productivity Paradox,” Goldman Sachs Research, October 5, 2015.

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth
by Stuart Ritchie
Published 20 Jul 2020

And as soon as scientists start to artificially inflate these numbers by self-citation, coercive citation and other suspect practices, they lose their meaning as measures of scientific quality. They begin to say less about which scientists and which journals are the best, and more about which have the most single-minded focus on boosting their metrics. It’s a clear example of Goodhart’s Law: ‘when a measure becomes the target, it ceases to be a good measure’.55 As we’ve seen, these measures have very much become the explicit targets in our modern scientific culture, creating unforeseen consequences: a perverse incentive structure that favours meaningless metrics and superficial stats over replicability, rigour and genuine scientific progress.

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And yet somehow, they find themselves working in a system where these hollow and misleading metrics are prized above all else. At first, having numbers that can quantify a scientist’s, or a journal’s, level of contribution might seem scientifically appealing: objective quantification, after all, is one of the unique strengths of science. But as Goodhart’s Law states, once you begin to chase the numbers themselves rather than the principles that they stand for – in this case, the principle of finding research that makes a big contribution to our knowledge – you’ve completely lost your way. The fact that these metrics aren’t just the preserve of individual scientists jockeying for status but are woven into the fabric of both the university and publication systems, is yet another example of how badly the scientific system is failing in its cardinal purpose

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ABC News abortion Abu Ghraib prison abuse (2003) accidental discoveries Acta Crystallographica Section E acupuncture Afghan hounds Agence France-Presse AIDS (acquired immune deficiency syndrome) Alchemist, The (Bega) Alexander, Benita Alexander, Scott algorithms allergies Alzheimer, Aloysius Alzheimer’s Disease Amazon American Journal of Potato Research Amgen amygdala amyloid cascade hypothesis anaesthesia awareness Fujii affair (2012) outcome switching Anaesthesia & Analgesia animal studies antidepressants antipsychotics archaeology Arnold, Frances arsenic artificial tracheas asthma austerity Australia Austria autism aviation Babbage, Charles Bacon, Francis bacteria Bargh, John Bayer Bayes, Thomas Bayesian statistics BDNF gene Before You Know It (Bargh) Bega, Cornelis Begley, Sharon Belgium Bell Labs Bem, Daryl benzodiazepines bias blinding and conflict of interest De Vries’ study (2018) funding and groupthink and meaning well bias Morton’s skull studies p-hacking politics and publication bias randomisation and sexism and Bik, Elisabeth Bill & Melinda Gates Foundation Biomaterials biology amyloid cascade hypothesis Bik’s fake images study (2016) Boldt affair (2010) cell lines China, misconduct in Hwang affair (2005–6) Macchiarini affair (2015–16) meta-scientific research microbiome studies Morton’s skull studies Obokata affair (2014) outcome switching preprints publication bias replication crisis Reuben affair (2009) spin and statistical power and Summerlin affair (1974) Wakefield affair (1998–2010) biomedical papers bird flu bispectral index monitor black holes Black Lives Matter blinding blotting BMJ, The Boldt, Joachim books Borges, Jorge Luis Boulez, Pierre Boyle, Robert brain imaging Brass Eye vii British Medical Journal Brock, Jon bronchoscopy Broockman, David Brown, Nick Bush, George Walker business studies BuzzFeed News California Walnut Commission California wildfires (2017) Canada cancer cell lines collaborative projects faecal transplants food and publication bias and replication crisis and sleep and spin and candidate genes carbon-based transistors Cardiff University cardiovascular disease Carlisle, John Carlsmith, James Carney, Dana cash-for-publication schemes cataracts Cell cell lines Cell Transplantation Center for Open Science CERN (Conseil Européen pour la Recherche Nucléaire) chi-squared tests childbirth China cash-for-publication schemes cell line mix-ups in Great Famine (1959–1961) misconduct cases in randomisation fraud in chrysalis effect Churchill, Winston churnalism Cifu, Adam citations clickbait climate change cloning Clostridium difficile cochlear implants Cochrane Collaboration coercive citation coffee cognitive dissonance cognitive psychology cognitive tests coin flipping Colbert Report, The Cold War collaborative projects colonic irrigation communality COMPare Trials COMT gene confidence interval conflict of interest Conservative Party conspicuous consumption Cooperative Campaign Analysis Project (CCAP) ‘Coping with Chaos’ (Stapel) Cornell University coronavirus (COVID-19) Corps of Engineers correlation versus causation corticosteroids Cotton, Charles Caleb creationism Crowe, Russell Csiszar, Alex Cuddy, Amy CV (curriculum vitae) cyber-bullying cystic fibrosis Daily Mail Daily Telegraph Darwin Memorial, The’ (Huxley) Darwin, Charles Das, Dipak datasets fraudulent Observational publication bias Davies, Phil Dawkins, Richard De Niro, Robert De Vries, Ymkje Anna debt-to-GDP ratio Deer, Brian democratic peace theory Denmark Department of Agriculture, US depression desk rejections Deutsche Bank disabilities discontinuous mind disinterestedness DNA (deoxyribonucleic acid) domestication syndrome doveryai, no proveryai Duarte, José Duke University duloxetine Dutch Golden Age Dutch Organisation for Scientific Research Dweck, Carol economics austerity preprints statistical power and effect size Einstein, Albert Elmo Elsevier engineering epigenetics euthanasia evolutionary biology exaggeration exercise Experiment, The exploratory analyses extrasensory perception faecal transplants false-positive errors Fanelli, Daniele Festinger, Leon file-drawer problem financial crisis (2007–8) Fine, Cordelia Fisher, Ronald 5 sigma evidence 5-HT2a gene 5-HTTLPR gene fixed mindset Food and Drug Administration (FDA) Food Frequency Questionnaires food psychology Formosus, Pope foxes France Francis, Pope Franco, Annie fraud images investigation of motives for numbers Open Science and peer review randomisation Freedom of Information Acts French, Chris Fryer, Roland Fujii, Yoshitaka funding bias and fraud and hype and long-term funding perverse incentive and replication crisis and statistical power and taxpayer money funnel plots Future of Science, The (Nielsen) gay marriage Gelman, Andrew genetically modified crops genetics autocorrect errors candidate genes collaborative projects gene therapy genome-wide association studies (GWASs) hype in salami-slicing in Geneva, Switzerland geoscience Germany Getty Center GFAJ-1 Giner-Sorolla, Roger Glasgow Effect Goldacre, Ben Goldsmiths, University of London Golgi Apparatus good bacteria Good Morning America good scientific citizenship Goodhart’s Law Goodstein, David Google Scholar Górecki, Henryk Gould, Stephen Jay Gran Sasso, Italy grants, see funding Granularity-Related Inconsistency of Means (GRIM) grapes Great Recession (2007–9) Great Red Spot of Jupiter Green, Donald Gross Domestic Product (GDP) Gross, Charles ground-breaking results groupthink ‘Growth in a Time of Debt’ (Reinhart and Rogoff) growth mindset Guzey, Alexey gynaecology h-index H5N1 Haldane, John Burdon Sanderson Hankins, Matthew HARKing Harris, Sidney Harvard University headache pills heart attacks heart disease Heathers, James height Heilongjiang University Heino, Matti Henry IV (Shakespeare) Higgs Boson Hirsch, Jorge HIV (human immunodeficiency viruses) homosexuality Hong Kong Hooke, Robert Hossenfelder, Sabine Houston, Texas Hume, David Huxley, Thomas Henry Hwang, Woo-Suk hydroxyethyl starch hype arsenic life affair (2010) books correlation versus causation cross-species leap language and microbiome studies news stories nutrition and press releases spin unwarranted advice hypotheses Ig Nobel Prize images, fraudulent impact factor India insomnia International Journal of Advanced Computer Technology Ioannidis, John IQ tests Iraq War (2003–11) Italy Japan John, Elton Journal of Cognitive Education and Psychology Journal of Environmental Quality Journal of Negative Results in Biomedicine Journal of Personality and Social Psychology journals conflict of interest disclosure fraud and hype and impact factor language in mega-journals negligence and Open Science and peer review, see peer review predatory journals preprints publication bias rent-seeking replication studies retraction salami slicing subscription fees Jupiter Kahneman, Daniel Kalla, Joshua Karolinska Institute Krasnodar, Russia Krugman, Paul Lacon, or Many Things in Few Words (Cotton) LaCour, Michael Lancet Fine’s ‘feminist science’ article (2018) Macchiarini affair (2015–16) Wakefield affair (1998–2010) language Large Hadron Collider Le Texier, Thibault Lewis, Jason Lexington Herald-Leader Leyser, Ottoline Lilienfeld, Scott Loken, Eric Lost in Math (Hossenfelder) low-fat diet low-powered studies Lumley, Thomas Lysenko, Trofim Macbeth (Shakespeare) Macbeth effect Macchiarini, Paolo MacDonald, Norman machine learning Macleod, Malcolm Macroeconomics major depressive disorder Malaysia Mao Zedong MARCH1 Marcus, Adam marine biology Markowetz, Florian Matthew Effect Maxims and Moral Reflections (MacDonald) McCartney, Gerry McCloskey, Deirdre McElreath, Richard meaning well bias Measles, Mumps & Rubella (MMR) measurement errors Medawar, Peter medical research amyloid cascade hypothesis Boldt affair (2010) cell lines China, misconduct in collaborative projects Fujii affair (2012) Hwang affair (2005–6) Macchiarini affair (2015–16) meta-scientific research Obokata affair (2014) outcome switching pharmaceutical companies preprints pre-registration publication bias replication crisis Reuben affair (2009) spin and statistical power and Summerlin affair (1974) Wakefield affair (1998–2010) medical reversal Medical Science Monitor Mediterranean Diet Merton, Robert Mertonian Norms communality disinterestedness organised scepticism universalism meta-science Boldt affair (2010) chrysalis effect De Vries’ study (2018) Fanelli’s study (2010) Ioannidis’ article (2005) Macleod’s studies mindset studies (2018) saturated fats studies spin and stereotype threat studies mice microbiome Microsoft Excel Milgram, Stanley Mill, John Stuart Mindset (Dweck) mindset concept Mismeasure of Man, The (Gould) Modi, Narendra money priming Mono Lake, California Moon, Hyung-In Morton, Samuel Motyl, Matt multiverse analysis nanotechnology National Academy of Sciences National Aeronautics and Space Administration (NASA) National Institutes of Health National Science Foundation Nature cash-for-publication and cell line editorial (1981) impact factor language in Obokata affair (2014) Open Access and open letter on statistical significance (2019) replication research Schön affair (2002) Stapel affair (2011) Nature Neuroscience Nature Reviews Cancer NBC negligence cell line mix-ups numerical errors statistical power typos Netflix Netherlands replication studies in Stapel’s racism studies statcheck research neuroscience amyloid cascade hypothesis collaborative projects Macleod’s animal research studies replication crisis sexism and statistical significance and Walker’s sleep studies neutrinos New England Journal of Medicine New York Times New Zealand news media Newton, Isaac Nielsen, Michael Nimoy, Leonard No Country for Old Men Nobel Prize northern blots Nosek, Brian Novella, Steven novelty Novum Organum (Bacon) Nuijten, Michèle nullius in verba, numerical errors nutrition Obama, Barack obesity Obokata, Haruko observational datasets obstetrics ocean acidification oesophagus ‘Of Essay-Writing’ (Hume) Office for Research Integrity, US Oldenburg, Henry Open Access Open Science OPERA experiment (2011) Oransky, Ivan Orben, Amy Organic Syntheses organised scepticism Osborne, George outcome-switching overfitting Oxford University p-value/hacking alternatives to Fine and low-powered studies and microbiome studies and nutritional studies and Open Science and outcome-switching perverse incentive and pre-registration and screen time studies and spin and statcheck and papers abstracts citations growth rates h-index introductions method sections results sections salami slicing self-plagiarism university ranks and Parkinson’s disease particle-accelerator experiments peanut allergies peer review coercive citation fraudulent groupthink and LaCour affair (2014–15) Preprints productivity incentives and randomisation and toxoplasma gondii scandal (1961) volunteer Wakefield affair (1998–2010) penicillin Peoria, Illinois Perspectives in Psychological Science perverse incentive cash for publications competition CVs and evolutionary analogy funding impact factor predatory journals salami slicing self-plagiarism Pett, Joel pharmaceutical companies PhDs Philosophical Transactions phlogiston phosphorus Photoshop Physical Review physics placebos plagiarism Plan S Planck, Max plane crashes PLOS ONE pluripotency Poehlman, Eric politics polygenes polyunsaturated fatty acids Popper, Karl populism pornography positive feedback loops positive versus null results, see publication bias post-traumatic stress disorder (PTSD) power posing Prasad, Vinay pre-registration preclinical studies predatory journals preprints Presence (Cuddy) press releases Prevención con Dieta Mediterránea (PREDIMED) priming Princeton University Private Eye probiotics Proceedings of the National Academy of Sciences prosthetic limbs Przybylski, Andrew psychic precognition Psychological Medicine psychology Bargh’s priming study (1996) Bem’s precognition studies books Carney and Cuddy’s power posing studies collaborative projects data sharing study (2006) Dweck’s mindset concept Festinger and Carlsmith’s cognitive dissonance studies Kahneman’s priming studies LaCour’s gay marriage experiment politics and preprints publication bias in Shanks’ priming studies Stanford Prison Experiment Stapel’s racism studies statistical power and Wansink’s food studies publication bias publish or perish Pubpeer Pythagoras’s theorem Qatar quantum entanglement racism Bargh’s priming studies Morton’s skull studies Stapel’s environmental studies randomisation Randy Schekman Reagan, Ronald recommendation algorithms red grapes Redfield, Rosemary Reflections on the Decline of Science in England (Babbage) Reinhart, Carmen Rennie, Drummond rent-seeking replication; replication crisis Bargh’s priming study Bem’s precognition studies biology and Carney and Cuddy’s power posing studies chemistry and economics and engineering and geoscience and journals and Kahneman’s priming studies marine biology and medical research and neuroscience and physics and Schön’s carbon-based transistor Stanford Prison Experiment Stapel’s racism studies Wolfe-Simon’s arsenic life study reproducibility Republican Party research grants research parasites resveratrol retraction Arnold Boldt Fujii LaCour Macchiarini Moon Obokata Reuben Schön Stapel Wakefield Wansink Retraction Watch Reuben, Scott Reuters RIKEN Rogoff, Kenneth romantic priming Royal Society Rundgren, Todd Russia doveryai, no proveryai foxes, domestication of Macchiarini affair (2015–16) plagiarism in salami slicing same-sex marriage sample size sampling errors Sanna, Lawrence Sasai, Yoshiki saturated fats Saturn Saudi Arabia schizophrenia Schoenfeld, Jonathan Schön, Jan Hendrik School Psychology International Schopenhauer, Arthur Science acceptance rate Arnold affair (2020) arsenic life affair (2010) cash-for-publication and Hwang affair (2005) impact factor LaCour affair (2014–15) language in Macbeth effect study (2006) Open Access and pre-registration investigation (2020) replication research Schön affair (2002) Stapel affair (2011) toxoplasma gondii scandal (1961) Science Europe Science Media Centre scientific journals, see journals scientific papers, see papers Scientific World Journal, The Scotland Scottish Socialist Party screen time self-citation self-correction self-plagiarism self-sustaining systems Seoul National University SEPT2 Sesame Street sexism sexual selection Shakespeare, William Shanks, David Shansky, Rebecca Simmons, Joseph Simonsohn, Uri Simpsons, The skin grafts Slate Star Codex Sloan-Kettering Cancer Institute Smaldino, Paul Smeesters, Dirk Smith, Richard Snuppy social media South Korea Southern blot Southern, Edwin Soviet Union space science special relativity specification-curve analysis speed-accuracy trade-off Spies, Jeffrey spin Springer Srivastava, Sanjay Stalin, Joseph Stanford University Dweck’s mindset concept file-drawer project (2014) Prison Experiment (1971) Schön affair (2002) STAP (Stimulus-Triggered Acquisition of Pluripotency) Stapel, Diederik statcheck statistical flukes statistical power statistical significance statistical tests Status Quo stem cells Stephen VI, Pope stereotype threat Sternberg, Robert strokes subscription fees Summerlin, William Sweden Swift, Jonathan Swiss Federal Institute of Technology Sydney Morning Herald Symphony of Sorrowful Songs (Górecki) t-tests Taiwan taps-aff.co.uk tax policies team science TED (Technology, Entertainment, and Design) Texas sharpshooter analogy Thatcher, Margaret theory of special relativity Thinking, Fast and Slow (Kahneman) Thomson Reuters Tilburg University Titan totalitarianism toxoplasma gondii trachea translational research transparency Tribeca Film Festival triplepay system Trump, Donald trust in science ‘trust, but verify’ Tumor Biology Turkey Tuulik, Julia Twitter typos UK Reproducibility Network Ulysses pact United Kingdom austerity cash-for-publication schemes image duplication in multiverse analysis study (2019) National Institute for Health Research pre-registration in Royal Society submarines trust in science university ranks in Wakefield affair (1998–2010) United States Arnold affair (2020) arsenic life affair (2010) austerity Bargh’s priming study (1996) Bem’s precognition studies California wildfires (2017) Carney and Cuddy’s power posing studies Center for Nutrition Policy and Promotion climate science in creationism in Das affair (2012) De Vries’ drug study (2018) Department of Agriculture Dweck’s mindset concept Fryer’s police brutality study (2016) image duplication in Kahneman’s priming studies LaCour affair (2014–15) Morton’s skull studies Office for Research Integrity Poehlman affair (2006) pre-registration in public domain laws Reuben affair (2009) Stanford Prison Experiment Summerlin affair (1974) tenure Walker’s sleep studies Wansink affair (2016) universalism universities cash-for-publication schemes fraud and subscription fees and team science University College London University of British Columbia University of California Berkeley Los Angeles University of Connecticut University of East Anglia University of Edinburgh University of Hertfordshire University of London University of Pennsylvania unsaturated fats unwarranted advice vaccines Vamplew, Peter Vanity Fair Vatican Vaxxed Viagra vibration-of-effects analysis virology Wakefield, Andrew Walker, Matthew Wansink, Brian Washington Post weasel wording Weisberg, Michael Wellcome Trust western blots Westfall, Jake ‘Why Most Published Research Findings Are False’ (Ioannidis) Why We Sleep (Walker) Wiley Wiseman, Richard Wolfe-Simon, Felisa World as Will and Presentation, The (Schopenhauer) World Health Organisation (WHO) Yale University Yarkoni, Tal Yes Men Yezhov, Nikolai Z-tests Ziliak, Stephen Zimbardo, Philip Zola, Émile About the Author Stuart Ritchie is a lecturer in the Social, Genetic and Developmental Psychiatry Centre at King’s College London.

Social Life of Information
by John Seely Brown and Paul Duguid
Published 2 Feb 2000

Those who are unhappy, by contrast, can express their rage by posting messages to the microsoft.crash.crash.crash or microsoft. sucks newsgroups. 35. The car, by the way, found a buyer within a week. 36. Lyman, 1997. 37. Quittner, 1995. 38. Leonard, 1997, p. 192. 39. "Collaborative tracking" of this sort can easily fall prey to Goodhart's law, which states that statistical regularities break down when used for control. Because the digital footprints people leave across the Web can provide guidance, people have an interest in creating fake footprints. 40. Markoff, 1999. 41. See chapter 1 for our discussion of Moore's Law solutions. 42.

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back
by Bruce Schneier
Published 7 Feb 2023

Although the 2013 Violence Against Women Act partially closed this vulnerability, a 2019 reauthorization was derailed by the gun lobby for reasons having nothing to do with this particular provision. 28 Hacking Bureaucracy When you design a set of rules, it’s common for those who must comply with them to optimize their actions to fit within the rules—even if what they end up doing goes against the expressly stated goal of those rules. Examples include an exterminator who releases an insect swarm to drum up business or a teacher who teaches strictly to the test to increase student test scores. Economists refer to this as Goodhart’s law: when a measure becomes a target, it stops being a good measure. In this manner, bureaucratic rules are hacked all the time by people who don’t want to abide by them. Bureaucracies are hacked from below, by those who are constrained by them, in order to get things done in spite of them. In the 1980s, Administrator Daniel Goldin hacked the normally moribund NASA bureaucracy and found loopholes in the regulations that applied to NASA in order to launch more, and cheaper, space probes like the Mars Pathfinder mission.

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Cornman, 113 explainability problem, 212–15, 234 exploits, 21, 22 externalities, 63–64 Facebook, 184, 236, 243 facial recognition, 210, 217 fail-safes, 61, 67 Fairfield, Joshua, 248 fake news, 81 Fate of the Good Soldier Švejk during the World War, The (Hašek), 116 fear, 195–97 Federal Deposit Insurance Corporation (FDIC), 96 Federal Election Campaign Act (1972), 169 federal enclaves, 113–14 Fifteenth Amendment, 161, 164 filibuster, 154–55 financial exchange hacks, 79–82, 83–85 Financial Industry Regulatory Authority, 84 financial system hack normalization as subversive, 90–91 banking, 75, 76–77, 119, 260n financial exchange hacks, 84, 85 index funds, 262n innovation and, 72, 90 wealth/power and, 119 financial system hacks AI and, 241–43, 275n banking, 74–78, 119, 260n financial exchanges, 79–82, 83–85 identifying vulnerabilities and, 77–78 medieval usury, 91 See also financial system hack normalization Fischer, Deb, 190 Fitting, Jim, 1 flags of convenience, 130 foie gras bans, 113–14 foldering, 26 food delivery apps, 99, 124 Ford, Martin, 272n foreknowledge, 54 Fourteenth Amendment, 141 Fourth Amendment, 136 Fox News, 197 frequent-flier hacks, 38–40, 46 Friess, Foster, 169 front running, 80, 82 Fukuyama, Francis, 140 Gaedel, Ed, 41 gambling, 186 gambrel roof, 109 GameStop, 81 Garcia, Ileana, 170 Garland, Merrick, 121 General Motors, 104 genies, 232–33 geographic targeting orders, 87–88 gerrymandering, 165–66 “get out of jail free” card, 260n Getty, Paul, 95 Ghostwriter, 201 gig economy, 99, 100, 101, 116, 123–25, 264n Go, 212, 241 Gödel, Kurt, 25, 27 Goebbels, Joseph, 181 Goldin, Daniel, 115 Goodhart’s law, 115 Google, 185 GPT-3, 220 Great Depression, 74 Great Recession, 96, 173–74 Greensill Capital, 102 Grossman, Nick, 245 Grubhub, 99 Hacker Capture the Flag, 228 hackers competitions for, 228 motivations of, 47 types, 22 hacking as parasitical, 45–47, 84, 173 by the disempowered, 103, 119, 120, 121–22, 141 cheating as practicing for, 2–3 context of, 157–60, 237 defined, 1–2, 9–12, 255n destruction as result of, 172–75 existential risks of, 251–52 hierarchy of, 200–202 innovation and, 139–42, 158–59, 249–50, 252 life cycle of, 21–24 public knowledge of, 23, 256n ubiquity of, 25–28 hacking defenses, 48–52, 53–57 accountability and, 67–68 AI hacking and, 236–39 cognitive hacks and, 53–54, 182, 185, 198–99 detection/recovery, 54–56 economic considerations, 63 governance systems, 245–48 identifying vulnerabilities, 56–57, 77–78, 237–38 legislative process hacks and, 147–49, 151, 154, 156 reducing effectiveness, 53–54, 61 tax hacks and, 15–16 threat modeling, 62–63, 64 See also patching hacking normalization as subversive, 90–91 casino hacks, 35–36, 37 hacking as innovation and, 158–59 “too big to fail” hack, 97–98 wealth/power and, 73, 104, 119, 120, 122 See also financial system hack normalization Hadfield, Gillian, 248 Han, Young, 170 Handy, 124 Harkin, Tom, 146 Harris, Richard, 35 Hašek, Jaroslav, 116 Haselton, Ronald, 75 hedge funds, 82, 275n Herd, Pamela, 132 HFT (high-frequency trading), 83–85 hierarchy of hacking, 200–202 high-frequency trading (HFT), 83–85 hijacking, 62 Holmes, Elizabeth, 101 hotfixes, 52 Huntsman, Jon, Sr., 169 illusory truth effect, 189 Independent Payment Advisory Board (IPAB), 153–54 “independent spoiler” hack, 169–70 index funds, 262n indulgences, 71–72, 73, 85, 260n innovation, 101, 139–42, 158–59, 249–50, 252 insider trading, 79–80 intention ATM hacks and, 32 definition of hacking and, 2, 10, 16 definition of system and, 19 Internet, 64–65 See also social media Internet of Things (IoT) devices bugs in, 14 patching for, 23, 49 reducing hack effectiveness in, 54 Intuit, 190 Investment Company Act (1940), 82 Jack, Barnaby, 34 jackpotting, 33–34 Jaques, Abby Everett, 233 Joseph Weizenbaum, 217 jurisdictional rules, 112–13, 128–31 Kemp, Brian, 167 Keynes, John Maynard, 95 Khashoggi, Jamal, 220 King Midas, 232 labor organizing, 115–16, 121–22 Law, John, 174 laws accountability and, 68 definition of hacking and, 12 market and, 93 rules and, 18, 19 threat model shifts and, 65 See also legal hacks; tax code legal hacks, 109–11 bureaucracy and, 115–18 common law as, 135–38 Covid-19 payroll loans and, 110–11 loopholes and, 112–14 tax code and, 109–10 legislative process hacks, 145–49 defenses against, 147–49, 151, 154, 156 delay and delegation, 153–56 lobbying and, 146–47 must-pass bills, 150–52 vulnerabilities and, 147–48, 267n Lessig, Lawrence, 169 Levitt, Arthur, 80 literacy tests, 162 lobbying, 77, 78, 146–47, 158 lock-in, 94 loopholes deliberate, 146 legal hacks and, 112–14 systems and, 18 tax code and, 15, 16, 120 See also regulation avoidance loot boxes, 186 Luther, Martin, 72 luxury real estate hacks, 86–88 Lyft, 101, 123, 125 machine learning (ML) systems, 209 Malaysian sharecropping hacks, 116 Manafort, Paul, 26 Mandatory Worldwide Combined Reporting (MWCR), 129 mansard roof, 109 market hacks capitalism and, 92–93 market elements and, 93–94 private equity, 101–2 “too big to fail,” 95–98 venture capital as, 99–101 Mayhem, 228–29 McSorley, Marty, 44 medical diagnosis, 213 medieval usury hacks, 91 Meltdown, 48 MercExchange, 137 microtargeting, 184, 185, 216 Mihon, Jude (St.

Radical Uncertainty: Decision-Making for an Unknowable Future
by Mervyn King and John Kay
Published 5 Mar 2020

The idea of reflexivity was developed by the Austrian émigré philosopher Karl Popper and became central to the thinking of Popper’s student, the highly successful hedge fund manager George Soros. 2 And it would form part of the approach to macroeconomics of the Chicago economist Robert Lucas and his followers, which we describe in chapter 19 , although their perspective on the problem and its solution would be very different. Reflexivity undermines stationarity. This was the essence of ‘Goodhart’s Law’ – any business or government policy which assumed stationarity of social and economic relationships was likely to fail because its implementation would alter the behaviour of those affected and therefore destroy that stationarity. 3 In an early illustration of reflexivity, Jonah prophesied the destruction of Nineveh, having received inside information concerning God’s plans to punish the city (his journey to Nineveh was interrupted by a bizarre encounter with a whale).

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Scott, 285 ; The Great Gatsby , 220 Flood, Merrill, 248–9 football, 265 , 267–8 , 269 , 270 , 272–3 Ford Jr, Henry, 298 forestry, ‘scientific’, 167 Forth Bridge, 33 fossil fuels, 360–1 , 363 Fowler, Norman, 291 fractal geometry, 238–9 Franklin, Benjamin, 383 French Revolution, 199–200 Friedman, Milton, 111–12 , 113 , 114 , 125 , 133 , 135 , 137 , 191 , 307 , 437 ; billiards analogy, 257–8 , 267–8 ; denies risk-uncertainty distinction, 74 , 400 , 420 ; on premises/assumptions of theory, 258–9 ; Price Theory – a Provisional Text , 73–4 , 113–14 Frisch, Ragnar, 134 , 137 Fritz Haber, 361 Fuld, Dick, 267 , 412 Galen (Greek physician), 398 Galileo, 389 Gallipoli expedition, 168 Gallup, George, 240–1 gambling, 37–8 , 53–4 , 55 , 106–9 , 114–16 , 125–6 , 168 , 169–70 ; addiction to, 83 ; ‘pignistic probability’, 78–84 , 438 game theory, 111 , 248–9 , 274 , 281 Gardner, Daniel, 294–5 Gardner, Martin, 77 , 139 Garner, Joel, 264–5 Gates, Bill, 28 , 29 , 30 , 227 , 427 Geertz, Clifford, 192 General Election, UK (2015), 242 General Election, UK (2017), 242 General Electric, 276 General Motors, 286–7 , 412 the Getty kouros, 269–70 , 271 Gettysburg, Battle of (1863), 188 Gibbon, Edward, 54 , 186 , 187 Gigerenzer, Gerd, 66–8 , 152 , 206 Gilboa, Itzhak, 139 Gilboa test, 148–9 Gladwell, Malcolm, Blink , 269–70 HMS Gloucester , 270 , 271 , 272 Go (game), 173–4 , 175 Godwin, William, 359 gold rush, Californian (1849), 48–9 gold standard, return to (1925), 25–6 , 269 Goldman Sachs, 6–7 , 9 , 20 , 68 , 202 , 246–7 golf, 85–6 ‘Goodhart’s Law’, 36 Google, 387 , 395–6 Gordon, Alexander, 282 gorilla, invisible, 140 Gossett, W. J., 71 Graham, Benjamin, 82–3 , 335–6 Grand Banks of Newfoundland, 368–9 , 423 , 424 Graunt, John, 56 , 69 , 232 , 328 , 383 Great Depression, 5 , 15 , 240 , 338 , 348 Great Divergence, 419–20 Greece, classical, 53–4 , 142 Greene, Graham, 438 Greenspan, Alan, 260 , 317–18 Groopman, Jerome, How Doctors Think , 184 Groundhog Day (film, 1993), 419 Gulf War (1991), 270 , 271 , 272 Hahn, Frank, 344–5 Halifax, Lord, 25 Hall, Monty, 62–3 , 65 Halley, Edmond, 56 , 57 Hamilton (musical), 216 , 217 Hamilton, W.

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J., 198 , 201 , 203–4 , 206 , 217 Singell, Larry, 74 , 78 Slaughter, Anne-Marie, 214 Sloan, Alfred, 286–7 , 412 small world models: Arrow–Debreu world, 343–5 ; and behavioural economics experiments, 116 , 141–7 ; and classical statistics, 247 ; and engineering, 352–6 ; and framing of problems, 261 , 362 , 398–400 ; and legal reasoning, 203 , 204 ; and machine intelligence, 173–7 , 185 , 263 ; of Malthus and Jevons, 358–62 ; maps as not the territory, 391–4 ; and narratives, 249–61 , 303–4 , 307–10 , 320–1 , 346 , 385 , 397 ; and non-human species, 274 ; as not ‘the world as it really is’, 96 , 100 , 252–5 , 261 , 309–10 , 320 , 342–5 , 346–51 , 352–5 , 376 , 399–400 ; and optimising behaviour, 112–13 , 116 , 129 – 30 , 155 , 166 , 170 , 334 , 382 , 399–400 ; and policy making, 346–9 ; and risk in finance theory, 421 ; and Savage’s analysis, 112–14 , 249 , 309–10 , 345 ; and styles of reasoning, 137–9 smartphones, 30–1 , 344 Smets, Philippe, 78–9 Smith, Adam, 163 , 254 , 343 , 382 , 387 ; The Wealth of Nations , 172 , 190 , 191 , 249 , 253 Smith, Ed, 263–4 Smith, John Maynard, 158 Snow, Dr John, 283 social choice theory, 440 social insurance, 161 , 192 , 427 social media, 351 social relationships: and altruism, 157 , 158 , 159–60 ; cooperation/collective intelligence, 155 , 162 , 176 , 231 , 272–7 , 279–82 , 343 , 412 , 413–17 , 432 ; economic advantages of cooperating, 159 , 160–1 ; and entrepreneurship, 431–2 ; and evolutionary science, 156–65 , 401 ; human capacity for communication/language, 159 , 161 , 162 , 172–3 , 216 , 272–7 , 408 ; mutualisation of risk, 160 , 162 , 192 , 325–6 ; networks of trust/cooperation/coordination, 17 , 155 , 272 , 274–6 , 432 ; process of forming expectations, 350–1 ; reciprocity in, 190–2 , 328 ; round of drinks phenomenon, 189–90 ; social class structure, 324 ; social kinship groups, 156 , 159–62 , 215–16 , 325 , 328–9 , 413–14 ; and trust, 162–3 , 165 social welfare, xiv–xv , 41 Socratic dialogue, 162 Solomon, King, 196 Solow, Robert, 42 Sony, 28 Soros, George, 36 , 319–20 , 336 South Korea, chaebol of, 276 South Sea bubble, 315 Soviet Union, 276 , 279 , 280 , 281 Spanish flu, 57 spectrum auctions, 257 Spence, Michael, 254 Spencer, Herbert, 157–8 Sperber, Dan, 162 , 272 , 415 St Athanasius, 99 St Francis, 116 , 127 , 130 , 167 Stalin, Joseph, 25 , 219 , 292 standard deviation, 234 Stanford, Leland, 48–9 , 427 Stanford University, 49 stationarity (mathematical/statistical term): as assumed in modelling, 333 , 339 , 340–1 , 349 , 350 , 366–7 , 371–2 , 382 ; and astronomical laws, 70 ; China and Japan’s turn inwards, 419–20 , 430 ; economics as ‘non-stationary’, 16 , 35–6 , 45–6 , 102 , 236 , 339–41 , 349 , 350 , 394–6 ; and the environment, 362 ; evolution as ‘non-stationary’, 407 , 428–9 , 430–1 ; financial sector as non-stationary, 16 , 202–3 , 268–9 , 320–1 , 331 , 333 , 339 , 366–8 , 402–3 , 406 ; and frequency distribution, 58 , 69–70 , 87 , 202 , 247 , 327 ; ‘Goodhart’s Law’, 36 ; and insurance underwriting, 327 ; and mortality tables, 57 , 69 ; and natural phenomena, 39 ; and opinion pollsters’ models, 242 ; and planetary motion, 18–19 , 35 , 373–4 , 392 , 394 ; and progress in science, 429–31 ; and reflexivity, 36 , 394 ; and resolvable uncertainties, 37 ; and risk-averse individuals, 306 ; and scientific reasoning, 18–19 , 35 , 236 , 373–4 , 388 , 392 , 429–31 ; and Value at risk models (VaR), 366–8 statistical discrimination, 207–9 , 415 statistics, xiii , xvi ; 25 standard deviation events, 6 , 68 , 235 , 331 , 366 ; bell-shaped ‘normal’ distribution, 57–8 , 233–5 , 237 ; classical statisticians, 58 , 247 ; false stories and bogus statistics, 242–6 ; frequency distribution, 38 , 40 , 57–8 , 69–70 , 72 , 86 , 87 , 202 , 247 ; lognormal distribution, 237 , 238 ; measures of central tendency , 237 ; models ignoring radical uncertainty, 15–16 ; opinion pollsters’ models, 240–2 , 390 ; power laws, 236–9 ; quota sampling, 240–1 ; random sampling, 234 , 239–41 ; ‘randomised controlled trials’ (RCTs), 243–5 ; regression analysis, 351 ; ‘scale invariance’, 238 ; standard deviation, 234 ; statistical distributions, 232–6 ; tails of ‘normal’ distribution, 14 , 40 , 166 , 233 , 234–5 , 401 ; see also probabilistic reasoning; subjective probabilities Stewkley church, 376 Stiglitz, Joseph, 254 Stockdale, Admiral James, 167–8 , 330 stomach ulcers, 284 Stoppard, Tom, Travesties , 89 strategy weekends, 180–3 , 194 , 296 , 407 string theory, 219 , 357 STS-119 space shuttle, 374 subjective probabilities: and 9 /11 terror attacks, 74–6 , 202 ; Appiah’s ‘cognitive angels’, 117–18 ; and belief in emerging scientific truth, 100 ; and Chicago School, 73–4 , 342–3 ; definition of term, 72 ; details of problem specification, 76–8 ; Ellsberg’s ‘ambiguity aversion’, 135 ; expected utility , 111–14 , 115–18 , 124–5 , 127 , 128–30 , 135 , 400 , 435–44 ; and extension of probabilistic reasoning, 71–2 ; Keynes and Knight, 72 ; and linguistic ambiguity, 98 , 100 ; and narrative complexity, 218–19 ; ‘pignistic probability’, 78–84 , 438 ; Ramsey describes, 73 ; ‘rational expectations theory, 342–5 , 346–50 ; small world-large world distinctions, 112–14 , 116 , 129–30 , 137–9 , 141–7 , 155 , 166 , 170 , 171 , 173–7 , 382 , 400 ; see also small world models; triumph over radical uncertainty, 15–16 , 20 , 72–84 , 110–14 ; two-child problem, 76–8 , 81 , 98 , 139 ; see also axiomatic rationality ‘sudden infant death syndrome’ (SIDS), 197–8 , 200–1 , 202 , 204 Suez crisis (1956), 174 Sumerians, 39 Survation, 242 Suter, Johann, 427 Sutter, John, 48–9 Swiss Re, 325–6 Switzerland, 418–19 , 426 , 428 Syrian conflict, 99 , 428 Tacoma Narrows Bridge collapse (1940), 33 , 341 Taleb, Nassim Nicholas, 14 , 38–9 , 166 , 422 , 438–9 Tay Bridge disaster (1879), 33 , 341 technological advances, 161 , 258 , 275–6 , 315 , 329 , 362 , 373–4 ; America’s innovative hegemony in, 427–8 ; and evolutionary science, 429 , 430 , 431 Tehran embassy siege (1979), 8 terrorism, 7 , 74–6 , 202 , 220 , 230 , 296 Tetlock, Philip, 21–2 , 221–2 , 294–5 Thaler, Richard, 118 , 148 Thales of Miletus, 303–4 , 319 , 320 , 422 Thames embankments, London, 424–5 Thatcher, Margaret, 290–2 , 412 Theranos, 228–9 Thiel, Peter, 361–2 , 427 The Third Man (film, 1949), 418–19 Thompson, Warren, 359 Thorp, Edward, 38 , 83 Tinbergen, Jan, 134 , 341 , 346 Tolkien, J.

Late Bloomers: The Power of Patience in a World Obsessed With Early Achievement
by Rich Karlgaard
Published 15 Apr 2019

Campbell formulated what’s come to be called Campbell’s Law, which asserts that “the more any quantitative social indicator is used for social decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social process it is intended to monitor.” In other words, the more important we make the SAT and its ilk, the more corrupt and distorted the results become. And the British economist Charles Goodhart formulated Goodhart’s Law, which states, “Any measure used for control is unreliable.” Put another way: Once attaining a high score becomes the goal of a measurement, the measurement is no longer valid. Put even more simply, and crassly: Anything that is measured and rewarded will be gamed. These two statistical realities create a perverse outcome.

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When the focus becomes taking the test—rather than the years of learning and development—the test no longer measures what it was created to quantify. Instead, it becomes a competition against a clock, a test of one’s ability to answer multiple-choice questions in a specific amount of time. As Goodhart’s Law makes clear, the more we incentivize test results, the more people will beg, borrow, and steal to game the tests. So people with the economic resources for private tutoring and extensive test preparation will attain significantly higher scores—without having actually learned much more about the subjects being tested.

Paper Promises
by Philip Coggan
Published 1 Dec 2011

The various monetary aggregates grew rapidly; the authorities responded with their only real weapon, which was to raise interest rates. This succeeded in causing a recession in the early 1980s and the destruction of a fair chunk of the British (and American) manufacturing sectors, but had much less success in controlling the money supply numbers. A British economist, Charles Goodhart, coined ‘Goodhart’s Law’, which was that any economic variable was doomed to misbehave as soon as it was targeted. It was like pinning jelly to the wall. The key point, perhaps, is that the amount of money has tended to expand as each new form has been introduced. William Jennings Bryan has triumphed, albeit posthumously.

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Business Week Butler, Eamonn Calder, Lendol California Callaghan, Jim Calvin, John Canada Canadian Tar Sands capital controls capital economics capital flows capital ratios carried interest carry trade Carville, James Cassano, Joseph Cato Institute Cayne, Jimmy CDU Party ‘Celtic tiger’ central bank reserves Cesarino, Filippo ‘Chapter’ Charlemagne Charles I, King of England cheques/checks chief executive pay Chile China Churchill, Winston civil war (English) civil war (US) Citigroup clearing union Clientilism Clinton, Bill CNBC collateralized debt obligations commerical banks commercial property commodity prices Compagnie D’Occident comparative advantage conduits confederacy Congdon, Tim Congress, US Connally, John Conservative Party Consols Constantine, Emperor of Rome consumer price inflation continental bonds convergence trade convertibility of gold suspended Coolidge, Calvin copper Cottarelli, Carlo Council of Nicea Cowen, Brian cowrie shells Credit Anstalt credit cards credit crisis of 2007 – 8 credit crunch credit default swaps ‘cross of gold’ speech Cunliffe committee Currency Board currency wars Dante Alighieri David Copperfield Davies, Glyn debasing the currency debit cards debt ceiling debt clock debt deflation spiral debt trap debtors vs creditors, battle defaults defined contribution pension deflation Defoe, Daniel Delors, Jacques Democratic convention of 1896 Democratic Party Democratic Republic of Congo demographics denarii Denmark deposit insurance depreciation of currencies derivatives Deutsche Bank Deutschmark devaluation Dickens, Charles Dionysius of Syracuse Dodd – Frank bill dollar, US Dow Jones Industrial Average drachma Duke, Elizabeth Dumas, Charles Duncan, Richard Durst, Seymour Dutch Republic East Germany East Indies companies Economist Edward III, King of England Edwards, Albert efficient-market theory Egypt Eichengreen, Barry electronic money embedded energy energy efficiency estate agents Estates General Ethelred the Unready euro eurobonds eurodollar market European Central Bank European Commission European Financial Stability Facility European Monetary System European Union eurozone Exchange Rate Mechanism, European exorbitant privilege farmers Federal Reserve Federal Reserve Bank of Philadelphia Federalist party fertility rate ‘fiat money’ Fiji final salary pension Financial Services Authority Financial Times Finland First Bank of the United States First World War fiscal policy fiscal union Fisher, Irving fixed exchange rates floating currencies florin Florio, Jim Ford, Gerald Ford, Henry Ford Motor Company Foreign & Colonial Trust foreign direct investment foreign exchange reserves Forni, Lorenzo Forsyte Saga France Francis I, King of France Franco-Prussian War Franklin, Benjamin French Revolution Friedman, Milton Fuld, Dick futures markets Galbraith, John Kenneth Galsworthy, John GATT Gaulle, Charles de Geithner, Tim General Electric General Motors general strike of 1926 Genghis Khan Genoa conference George V, King of England Germany gilts Gladstone, William Glass – Steagall Act Gleneagles summit Glorious Revolution GMO Gokhale, Jagadeesh gold gold exchange standard gold pool gold standard Goldman Sachs goldsmiths Goodhart, Charles Goodhart’s Law Goschen, George Gottschalk, Jan government bonds government debt Graham, Frank Granada Grantham, Jeremy Great Compression Great Depression Great Moderation Great Society Greece Greenspan, Alan Gresham, Sir Thomas Gresham’s Law Gross, Bill G7 nations G20 meeting Guinea Habsburgs Haiti Haldane, Andrew Hamilton, Alexander Hammurabi of Babylon Havenstein, Rudolf von Hayek, Friedrich Heavily Indebted Poor Countries initiative hedge funds Henderson, Arthur Henry VIII, King of England Hien Tsung, Chinese emperor Hitler, Adolf Hoar, George Frisbie Hohenzollern monarchy Holy Roman Empire Homer, Sydney Hoover, Herbert House of Representatives houses Hume, David Hussein, Saddam Hutchinson, Thomas Hyde, H.

Breakout Nations: In Pursuit of the Next Economic Miracles
by Ruchir Sharma
Published 8 Apr 2012

When they are on a good run they tend to overshoot and create the conditions for their own demise. Popular understanding tends to lag well behind the reality: by the time a regime’s rules have been codified by experts and hashed over in the media, it is likely already in decline. That dynamic underpins Goodhart’s law (a cousin of Murphy’s law), coined by former Bank of England adviser Charles Goodhart: once an economic indicator gets too popular, it loses its predictive value. In a period of impending change, like this one, with the painful ending of a golden age of easy money and easy growth, it is typical for people to cling to dated ideas and rules for too long, particularly notions that minimize or explain away potential risks.

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Kospi,” 153, 164 drug cartels, 74, 79–80 Dubai, 188, 214, 218–19 Dubai World, 214 Dubrovnik, 97 Dun Qat refinery, 201 DuPont, 9 “Dutch disease,” 179, 220 earnings, corporate, 3 East African Community (EAC), 208–9 East Asia, 8, 10, 46, 131–32, 146, 196–97, 208, 245 “East Asian tigers,” 8, 10, 146 “easy money,” 5–6, 11, 13–14, 38, 105, 133, 176–77, 182–83 economic transformation program (ETP), 151 economies: agrarian, 9, 17–18, 21, 22, 27 business cycles in, 2, 5–6, 11, 223 command-and-control, 29–30, 39, 156, 199–200 commodity, 133–34, 137–38, 223–39 counter-cyclical, 120–21 developing, see developing countries diversified, 165–66 emerging markets in, vii–x, 2–11, 37–38, 47, 64, 94, 185–91, 198–99, 242–49, 254–55, 259–62 forecasting on, x, 1–14, 17, 18, 31–32 global, 1–2, 4–5, 6, 7–8, 9, 12, 14, 18–19, 37, 38, 51–52, 68–71, 153–55, 158–59, 161, 167–69, 170, 176, 178, 183–91, 222–24, 228–31, 233–36, 241–42, 249–54 growth rates in, 8–11, 185–91, 244–49, 254–55 historical trends in, ix–x, 2, 9, 10–11 knowledge, 236–37 parallel, 79–80 recessions in, 5–6, 9, 11, 18, 34, 80, 101, 109, 131, 132, 144, 225, 244, 249–51 “tiger,” 8, 10, 46 volatility of, 249–51 see also specific countries Economist, 207 education, x, 5, 12, 22, 63, 65, 76, 121, 168–69, 206, 218, 220 efficiency, 63–64 “efficient corruption,” 135–37 Egypt: author’s visit to, ix corruption in, 217 economic reform in, 27, 28, 126–27, 217–18 as emerging market, 204, 235 foreign investment in, ix, 92 inflation rate in, 88 revolts in, 127, 216 stock market of, 190 Eighth Malaysian Plan, 151 Einstein, Albert, 238 El Beblawi, Hazem, 128 el dedazo (“the big finger”), 76–77 election cycles, 2 electromagnetic radiation, 17 “electronic wallet,” 208 Elle, 53 emerging markets, vii–x, 2–11, 37–38, 47, 64, 94, 185–91, 198–99, 242–49, 254–55, 259–62 energy efficiency, 226–27 energy sector, 5, 13, 51–52, 67–68, 82, 125, 170, 212–13, 215, 223, 224–29 English language, 37, 52–53, 141, 196, 203–4 entrepreneurship, 38, 43, 58, 96, 144, 166, 186, 225 entry point projects (EPPs), 151 environmental issues, 17, 135 Equatorial Guinea, 210 Erbakan, Necmettin, 114–15 Erdogan, Recep Tayyip, 111, 112, 113–14, 116–18, 123, 124–28, 210, 245 “errors and omissions,” 150 Eskom, 177 Estonia, 109 euro, 100, 105, 107, 108 Eurocentrism, 206 Europe: agriculture in, 231–32 banking system of, 12 Central and Eastern, 8, 11, 97–110, 121, 170, 203, 247 debt levels in, 57, 97, 100, 121–22, 252 economy of, 7, 12, 107–8, 230, 241, 245 foreign investment by, 2, 7, 20, 100, 104–8 foreign trade of, 145, 159 GDP in, 20, 100 government deficits in, 100 growth rate of, 6, 241, 242 manufacturing sector of, 247 political unity of, 11, 49, 53, 97–98, 208–9 recessions in, 101, 132 unemployment in, 101, 126 welfare states in, 63 see also specific countries European Community (EC), 208–9 European Union (EU), 11, 97–98, 101, 105, 106–8, 109, 115–16, 118, 121–22, 159, 253–54 Eurozone, 11, 99–100, 105, 106–8, 109, 121–22, 254 expatriate workers, 219 Facebook, 41 factories, 17–18, 22–23, 28, 43, 67, 68, 132, 230 “fairness creams,” 54 family enterprises, 125–26, 134–38, 155, 160, 161–63, 167–69, 254 “farmhouses,” vii–viii fast-food outlets, 53 Federal Palace Hotel, 212 Federal Reserve Board, 5–6, 222 feeder ships, 200 Femsa, 75 Fiat, 120 fiber-optic cables, 207–8 Fidesz Party, 104–5 Fiji, 4 film industry, 44, 47, 167, 186, 211 “financialization of commodities,” 227–28 Finland, 238, 251 First Coming, 243 fishing industry, 193 five-year plans, 20, 27, 150–51 Forbes, 47, 91 “forced listing,” 188 Ford, 75, 120 foreign investment, vii–x, 2, 7–8, 9, 18, 20, 32, 35–36, 37, 43–44, 49–50, 59, 63, 64, 66, 68–72, 86, 87, 91–94, 100, 104–8, 118, 119–20, 133–35, 137, 139, 140–41, 144, 146–50, 151, 183–84, 198–200, 201, 203–5, 206, 225 foreign trade, vii, x, 6, 7, 13, 18, 20–21, 23, 26, 28, 29, 31, 32–33, 43, 59, 61, 62, 67–68, 72, 75, 80, 83, 85, 86, 90, 117, 120, 122, 132, 133–34, 144–45, 147, 148, 157, 158–59, 162, 178, 183, 196–97, 198, 206, 220, 223, 226, 232, 233–34 Four Seasons Hotel, 111, 232, 233 Four Seasons Index, 232, 233 “$4,000 barrier,” 7–11 Fourth World, 185–91, 204–9, 220, 221 Fox, Vicente, 77 Fraga, Arminio, 72 France, 63, 100, 121, 123–24 Franklin, Benjamin, 214 Freedom House, 205 “free float,” 188 free markets, x, 8–9, 96, 104 French, Patrick, 47 French Riviera, 59–61 frontier markets, 89, 185–91, 213, 261–62 Fujian Province, 164 futures contracts, 5 Gandhi, Indira, 55 Gandhi, Rahul, 48 Gandhi, Sanjay, 55 Gandhi, Sonia, 39 Gandhi family, 39, 47–48, 55, 57 gas, natural, 13, 85, 179, 214, 215, 217, 225, 235 gasoline, 126, 215 GaveKal Dragonomics, 229 Gaziantep, 125 General Electric, 9 generators, electric, 212–13 Germany: billionaires in, 45 economy of, 103 as EU member, 107, 121 GDP of, 247 low-context society of, 40 manufacturing sector in, 157, 158–59, 247 population of, 37 public transportation in, 16 South Korea compared with, 168–69 Germany, East, 102 Gertken, Matthew, 29 Ghana, 187 Gibson, Mel, 129 Gini coefficient, 173 Girl’s Generation, 167 glass manufacturing, 221 Globo, 61 GM, 75, 163 “go-go stocks,” 3 gold, 3, 141, 176, 178, 179–80, 192, 202, 205, 224, 229–30 “Goldilocks economy,” 4, 5–6 gold shares, 179–80 gold standard, 178 Goldstone, Jack, 217 Goodhart, Charles, 11 Goodhart’s law, 11 Google, 41, 237–38 Gorbachev, Mikhail, 103 “Goulash Communism,” 101 government paper, 116 government spending, 41–42, 63, 65, 66–67, 70–71, 72, 86, 87–88, 109, 133, 181–83, 190 government transformation program (GTP), 151 graft, 43–44 see also corruption Great Britain: auto industry in, 31 empire of, 49, 118, 192 foreign investment by, 206 government of, 89 labor force of, 100 public transportation in, 16 socialism in, 150 Great Depression, 101, 109, 252–53 Great Moderation, 250–51 Great Recession (2008), 5–6, 9, 59, 66, 76, 80–81, 88, 92–93, 100, 101, 102, 103, 109, 119, 122, 131, 144, 180, 189, 225, 243, 247–49, 250, 254 Greece, 11, 27, 30, 99, 100, 107–8, 121, 181, 252 green revolution, 231–32 Greenspan, Alan, 6 gross domestic product (GDP), 1, 3–4, 6, 17, 18, 20, 26, 32, 43, 49, 57, 63, 65, 66, 67, 72, 85, 92, 100, 107, 110, 116, 117, 119, 120, 121, 131, 133, 139, 140, 141, 142, 144–45, 147, 149, 155, 157, 158, 159, 161, 165, 170, 173, 178–79, 180, 191, 206, 208, 210, 214, 215, 217, 218, 219, 228, 236, 243, 247, 252 Group of Twenty (G20), 215 growth corridors, 151 Gül, Abdullah, 118, 123–24, 127 Gulf States, 214–21, 244, 245 see also specific states Gupta, Anil K., 237 Habarana, 196 Hall, Edward, 39–40 Hallyu, 167 Hambantota, 197 Hanoi, 198, 200 Han people, 53 Harmony Gold, 180 Harvard School of Public Health, 241 Havel, Václav, 111 Hayek, Friedrich, 109 Hazare, Anna, 42–43 headscarves, 123–24 health care, 63 helicopters, 60, 64, 72 herd behavior, 8, 228–31 high-context societies, 39–40, 41, 47 high-speed trains, 15–16, 20, 21 highways, 17, 20, 21, 65, 231 Hindi language, 52–53, 56 “Hindu rate of growth,” 174 Hindustan Times, 53 Hirsch, Alan, 178 Ho Chi Minh City, 200, 201, 203 Honda, 161 Hong Kong, 9, 141, 235 “Hopeless Continent,” viii Hotel Indonesia Kempinksi, 129 hotels, 12, 31, 59–61, 65, 111, 232, 233 “hot money,” 149–50 housing prices, 5–6, 16, 18, 24–25, 28–29, 31, 32, 61, 92, 103–4 HP, 158 Huang, Yukon, 28 Huang Guangyu, 46 Hu Jintao, 29 Humala, Ollanta, 66–67 human-rights violations, 193 Hungary: banking in, 105 as breakout nation, 99–100, 101 economic growth of, 99, 104–6, 109 as emerging market, 104–6 as EU candidate, 100, 105 foreign investment in, 104, 105 GDP of, 100 growth rate of, 244 income levels of, 8 industrial production in, 101 political situation in, 104–5, 109 population of, 106 post-Communist era of, 101, 104 welfare programs of, 106 Hussein, Saddam, 195 Huxley, Aldous, x hyperinflation, 39, 42, 62, 66 “hypermarkets,” 90–91 Hyundai, 90, 156, 158, 161–63, 168 identification cards, 213 immigration, 79, 82, 85, 95 income: national levels of, 4, 8, 11, 16–21, 24–25, 31–32, 38, 58, 61, 63, 72, 75, 83, 86–87, 88, 97–98, 113, 116, 121, 138, 139–40, 141, 144, 145, 148, 153–55, 157, 173, 176–77, 182–83, 204 per capita, ix, 7–8, 11, 13, 19–21, 41, 58, 61, 63, 72, 73–75, 76, 88, 97–98, 109, 116, 127, 131–32, 138, 148, 176–77, 204, 207, 216, 244, 245–46 taxation of, 44, 51, 63, 76, 86, 106, 126–27, 182, 214, 221 India, 35–58 agriculture of, 38, 44, 54, 57 auto industry of, 54, 161, 162, 173 baby-boom generation in, 37–38 billionaires in, viii, 25, 44–47, 79, 254 Brazil compared with, 10, 39–43, 61, 70 as breakout nation, 38–39, 49 capitalism in, 38–39, 42, 46–47, 49, 50–51, 58 China compared with, 1, 10, 19, 25, 36, 37–38, 41, 45, 47, 52, 53, 56, 57, 58 consumer prices in, 38, 39, 49, 52–54, 57 corruption in, 42, 43–44, 45, 46–47, 49–51, 58 credit market in, 38, 51 debt levels in, 57–58 democracy in, 30, 48–49, 50, 55–56, 58 “demographic dividend” for, 37–38, 55–56, 58 domestic market of, 36, 43 economic reforms in, 28, 38–39, 49 economy of, 28, 35–58, 174, 204 elections in, 48–49, 50, 55 “Emergency” period of, 55–56 as emerging market, 3–4, 10, 30, 35–39, 43, 49, 106, 253 English spoken in, 37, 52–53 entrepreneurship in, 38, 43, 58 film industry of (Bollywood), 44, 47, 167, 211 forecasts about, 35–36, 37, 39–40 foreign investment in, vii–viii, 7, 35–36, 37, 43–44, 49–50, 183, 225 foreign trade of, vii, 43, 157 Gandhi family in, 39, 47–48, 55, 57 GDP of, 1, 3–4, 43, 49, 57 as global economy, 1, 37, 38, 51–52 government of, 30, 38–39, 41–43, 47–52, 55–58 government spending in, 41–42 growth rate of, 3–4, 9, 30, 35–58, 61, 64, 87, 88, 174, 241, 244 high-context society in, 39–40, 47 income levels of, 8, 19, 54, 58 independence of, 174, 175, 176 Indonesia compared with, 135, 136 inflation rate in, 39, 43–44, 248 infrastructure of, 10, 43, 51 investment levels in, 43–44, 49–50 labor market in, 38, 55 leadership of, 38–39, 41–42, 47–52, 57–58, 174 License Raj of, 38 middle class of, 42–43, 52–56 mining industry of, 44, 254 natural resources of, 51–52, 235 northern vs. southern, 49–52, 54, 58 outsourcing industry in, 141 parliament of, 43, 44, 47–49 political situation in, 30, 37, 38–39, 47–49, 50, 55–58, 174 population of, 19, 37–38, 52–56, 57, 58, 95 poverty in, 41–42, 52–53, 57–58 price levels in, 53 productivity in, 64 real estate market in, 44, 254 “rope trick” in, 35–36, 36, 37, 58 rural areas of, 38, 57 Russia compared with, 36–37, 44–45, 46, 87, 88, 95 social unrest in, 42–43, 55–56 Sri Lanka’s relations with, 196, 197 state governments of, 37, 44, 48–52 sterilization (vasectomy) program in, 55–56 stock market of, 36–37, 38, 70, 189, 243, 244 taxation in, 44, 51 technology sector of, 141, 166, 254 unemployment in, 41–42 wealth in, vii–viii, 25, 44–47, 57, 79 welfare programs of, 10, 41–42 India: A Portrait (French), 47 Indian Ocean, 197 Indonesia, 129–38 in Asian financial crisis, 131–35 banking in, 133–34, 135 billionaires in, 131–32 China compared with, 132–33, 135, 136 Chinese community in, 129 consumer prices in, 137–38, 232 corruption in, 134–35 currency of (rupiah), 131 economic reforms in, 132–38, 147 economy of, 28, 132–38, 147, 174, 254 elections in, 136–37 as emerging market, 133, 232 family enterprises in, 134, 138, 254 foreign investment in, 7, 133–35, 137 foreign trade of, 132, 133–34, 157, 159 GDP of, 131, 133 government of, 30, 132–37 growth rate of, 132–33, 136, 137, 245, 246, 254 income levels of, 8, 131–32, 138 India compared with, 135, 136 inflation rate of, 137–38, 249 labor market in, 23, 203 land development in, 135–36 national debt of, 134–35 natural resources of, 133–34, 159, 235 Philippines compared with, 132, 138, 140 political situation in, 129, 132, 133, 134, 135, 136, 137, 210 population of, 133, 136 Russia compared with, 137–38 urban decentralization in, 136–37 wealth of, 131–38 industrialization, 10, 67, 68, 101 inflation rate, x, 4, 5, 17, 22, 23, 24, 25, 31, 33, 39, 42, 43–44, 62, 66, 68–69, 88, 104, 115, 116, 118, 137–38, 176, 177, 179, 202, 226, 228, 247–49, 250, 254 Infosys, 37 infrastructure, x, 10, 15–16, 20–21, 43, 51, 61, 62, 64, 65, 69, 84–85, 88, 90–91, 116, 120–21, 199, 200–201, 239 inheritance taxes, 44 insider trading, 46, 187 Institutional Revolutionary Party (PRI), 76–78 Intel, 164, 203–4 intellectual property, 238 interbank loans, 150 interest rates, 6, 11, 62, 67, 68–70, 105, 106, 107, 115, 119, 120, 228–29, 247–49, 250 internal devaluation, 108, 109 International Finance Corporation, 214 International Monetary Fund (IMF), 101, 115, 160, 173, 208, 216–17 Internet, 2, 85, 173, 175, 177, 207–8, 220, 225, 230, 237–39 interregional exports, 206–7 investment, viii, x, 2–8, 19, 37, 90, 96, 131, 144, 146–50, 156, 160–61, 165, 190, 212–13, 220, 223–29, 231, 235, 236–38, 244 see also foreign investment Ipanema Beach, 21, 61, 65, 66 Iran, 10, 123, 189, 190 Iraq, 10, 122, 189, 195 iron, 51–52, 59, 67, 69, 180, 232 Iron Curtain, 101 Iskandar region growth agenda, 151 Islam, 111, 113–17, 119, 121, 122, 123–24, 127, 146, 162, 211, 219, 220, 246 Islamic Museum, 219 Israel, 122, 127 Istanbul, 111, 115, 122, 125, 146 Italy, 40, 99 Ivory Coast, 208 Izmir, 115, 124, 125, 146 Jaffna Peninsula, 193, 195 Jakarta, 129–31, 135, 136, 137, 232 Jalan Sudirman, 129 Japan: in Asian financial crisis, 155–56 auto industry of, 139, 144, 161 China compared with, 18, 20, 22, 24, 31, 32–33 currency of (yen), 32–33 democratic government of, 30 economic slowdown of, 22, 254 economy of, 8, 20, 22, 81, 90, 197, 230, 235, 242, 253, 254 foreign trade of, 7, 32–33, 144–45, 157, 159 GDP of, 144–45 growth rate of, 6, 32–33, 44, 235 income levels of, 20, 138, 144 inflation rate in, 31 manufacturing sector of, 157, 159, 170, 230, 235 pop culture in, 167 population of, 169 property values in, 24, 252 public transportation in, 20 real estate market in, 3 recession in, 109 research and development (R&D) in, 160–61, 237 social conformity in, 200 South Korea compared with, 153, 155–56, 157, 159, 160–61, 163, 164, 167, 168, 169, 170 stock market of, 156, 235 Taiwan’s relations with, 163–64 technology industry of, 160–61, 236–38 Thailand compared with, 139, 144–45 Java, 137 “Jeepneys,” 130, 138 Jews, 118, 149 Jharkhand, 46 Jiang Zemin, 29 Jobbik (Movement for a Better Hungary), 105 Jockey underwear, 54 Johannesburg, 181, 204 Jonathan, Goodluck, 209–11, 213 Jordan, 122 J-pop, 167 junk bonds, 228 “just-in-time” supply chains, 80 Kabila, Laurent, 205 Kagame, Paul, 206 Kano, 213 Kaohsiung, 136 Kapoor, Ekta, 41 Karachi, 190 Karnataka, 50, 51 Kashmir, 49, 50 Kasimpasa neighborhood, 125 Kayseri, 124 Kazakhstan, 30, 89, 93, 123, 212 Kazan, 85 Kennedy, John F., 129 Kenya, 191, 205, 209 Keynes, John Maynard, 109 KGB, 86 Khodorkovsky, Mikhail, 87 Kia, 161, 162–63 kidnappings, 78–79, 190–91 Kim Jong Il, 170 Kinshasa, 205 Kirchner, Cristina, 89 Kirchner, Nestor, 89 Klaus, Vaclav, 108 Koç family, 125 “Korea Discount,” 167–69 “Korean Wave,” 122, 167 KOSPI index, 70, 153, 155, 156, 164, 165 K-pop, 122, 154, 167 Kuala Lumpur, 147, 148, 151 Kumar, Nitish, 50–51 Kuwait, 187–88, 214, 216, 218, 219 Kuznets curve, 76 labor market, 7, 17, 21–23, 27, 32, 38, 47, 55, 64, 65, 76, 77, 102, 103, 104, 164, 169–70, 174–75, 179, 180–81, 199, 203–4, 246–47 Lada, 86 Lafarge, 213 Lagos, 211, 212, 213 landlines, 207 land-use laws, 25, 168 Laos, 188 laptop computers, 158, 164 large numbers, law of, 7 Last Train Home, The, 22–23 Latin America, viii, 40–41, 42, 73–75, 81, 89, 246 see also specific countries Latvia, 101 Lavoisier, Antoine, 235–36 law, rule of, x, 50–51, 89, 96, 127, 181–82 lead, 19 Leblon neighborhood, 61 Lee Kwan Yew, 118, 148, 193 Lehman Brothers, 164 Le Thanh Hai, 203 Lewis, Arthur, 21 “Lewis turning point,” 21 LG, 158, 163 “Liberation Tigers” of Tamil Eelam, 192–93, 197 Liberty, 178 Libya, 127, 216 Limpopo River, 171 Linux, 238 liquidity, 9, 228–30 liquor stores, 126 literacy rate, 52 Lithuania, 101, 109 Lixin Fan, 22–23 loans, personal, 12, 24, 116, 125, 150 long-run forecasting, 1–14 L’Oréal, 31 Louis Vuitton, 31 Lugano, 40 Lula da Silva, Inácio, 59, 61, 66, 70, 210, 226, 248 luxury goods, vii–viii, 12, 25, 31, 236 Macao, 201 macroeconomics, 7–8, 13, 66, 67, 145–46, 188 “macromania,” 7–8, 188 Made in America, Again, 246–47 “made in” label, 155, 246–47 Madhya Pradesh, 52 maglev (magnetic levitation) trains, 15–16, 231 Magnit, 90–91 Mahatma Gandhi National Rural Employment Guarantee Act (MGNREGA), 41–42 Malaysia, 146–52 in Asian financial crisis, 18, 131–32, 146–47, 149–50 banking in, 146, 149–50, 151, 252 currency of (ringgit), 131, 146–47, 149 economic planning in, 150–52, 161 economy of, 18, 118, 150–52, 161, 235 electronics industry of, 147–48 as emerging market, 10, 45, 118, 149, 161, 235 foreign investment in, 146–50, 151 foreign trade of, 6, 144, 147, 157 GDP of, 145, 147, 149 government of, 146, 148–52 growth rate of, 9, 147–48, 149, 244 income levels of, 138, 148 manufacturing sector in, 147–48, 150 political situation in, 146–49 Singapore compared with, 118 stock market of, 131, 235 Thailand compared with, 144, 145, 147 wealth of, 148 Mali, 208 Malta, 30, 106 Malthus, Thomas, 225, 231–32 Mandela, Nelson, 171, 172, 176 Manila, 130, 138, 139, 140, 141 Manuel, Trevor, 176 manufacturing sector, 17–18, 22–23, 28, 43, 54, 75, 80, 88–89, 90, 110, 124, 132, 147–48, 150, 155, 157, 158–59, 160, 161–66, 168, 170, 180, 221, 230, 235, 246–47, 265 Maoism, 37, 47 Mao Zedong, 21, 27, 29 Marcos, Ferdinand, 138, 139, 210 markets: black, 13–14, 96, 126 capital, 69, 70–71; see also capital flows commodity, 12, 13–14, 223–39 currency, 4, 9, 13, 28 domestic, 36, 43, 183 emerging, vii–x, 2–11, 37–38, 47, 64, 94, 185–91, 198–99, 242–49, 254–55, 259–62 free, x, 8–9, 96, 104 frontier, 89, 185–91, 213, 261–62 housing, 5–6, 16, 18, 24–25, 28–29, 31, 32, 61, 92, 103–4 labor, 7, 17, 21–23, 27, 32, 38, 47, 55, 64, 65, 76, 77, 102, 103, 104, 164, 169–70, 174–75, 179, 180–81, 199, 203–4, 246–47 see also stock markets Mato Grosso, 232 Mayer-Serra, Carlos Elizondo, 78 MBAs, 225 Mbeki, Thabo, 176, 206 Medellín drug cartel, 79 Medvedev, Dmitry, 95–96 Mercedes-Benz, 86, 144 Merkel, Angela, 108 Mexican peso crisis, 4, 9 Mexico, 73–82 antitrust laws in, 81–82 banking in, 81, 82 billionaires in, 45, 47, 71, 78–80 Brazil compared with, 71, 75 China compared with, 80, 82 consumer prices in, 75–76 corruption in, 76–77 currency of (peso), 4, 9, 73, 80, 131 drug cartels in, 79–80 economy of, 4, 12, 28, 73–82, 178, 183 emigration from, 79, 82 foreign exports of, 6, 75, 80, 158 GDP of, 76, 77, 81 government of, 76–78 growth rate of, 73–82, 244 income levels of, 8, 73–75, 76, 113 labor unions in, 76, 77 national debt of, 76, 80–81 nationalization in, 77–78 oil industry of, 75, 77–78, 82 oligopolies in, 73, 75, 76–82, 178 parliament of, 76–77 political situation in, 76–78, 82 population of, 73 stock market of, 73, 75, 76, 81 taxation in, 76 U.S. compared with, 75, 79, 80 Mexico City, 75 micromanagement, 151 middle class, 10, 19–20, 33, 42–43, 52–56, 182, 211, 236 Middle East, 38, 65, 68, 113, 116, 122, 123, 125, 166, 170, 189, 195, 214–21, 234, 246 middle-income barrier, 19–20, 144–45 middle-income deceleration, 20 Miller, Arthur, 223 minimum wage, 29, 63, 126, 137 mining industry, 44, 93, 154, 175, 176, 178–80 Miracle Year (2003), 3–6 misery index, 248–49 Mittal, Sunil Bharti, 204–5, 206, 209 mobile phones, 53, 86, 204–5, 207–8, 212, 237 Mohammed, Mahathir, 146–47, 148, 151 Moi, Daniel arap, 205 monetization, 225 Money Game, The (Smith), 234 Mongolia, 191 monopolies, 13, 73, 75–76, 178–79 Monroe, Marilyn, 129 Monte Carlo, 94 “morphic resonance,” 185 mortgage-backed securities, 5 mortgages, 5, 92, 105–6 Moscow, 12, 83, 84, 90, 91, 96, 136, 137, 232 mosques, 111 Mou Qizhong, 46 Mozambique, 184, 194–95, 198, 206 M-Pesa, 208 MTN, 212–13 Mubarak, Gamal, 218 Mubarak, Hosni, 92, 127, 218 Mugabe, Robert, 176, 181 Multimedia Supercorridor, 151 multinational corporations, 53, 73, 75, 81, 151, 158–59, 160, 184, 230 Mumbai, 43, 44, 79, 214, 244 Murder 2, 167 Murphy’s law, 11 Muslim Brotherhood, 127 Mutual, 178 mutual funds, 178–79 Myanmar, 30 Myspace, 41 Naipaul, V.

System Error: Where Big Tech Went Wrong and How We Can Reboot
by Rob Reich , Mehran Sahami and Jeremy M. Weinstein
Published 6 Sep 2021

Economists have long worried about the problem of proxies, especially in situations where employees receive incentives for meeting their targets. In such situations, employees will quickly orient themselves not toward the worthy end but toward the proxy. The metric becomes the goal, and the means justify the end. This is called Goodhart’s Law, which states that when a measure becomes a target, it ceases to be a good measure. A common sequence would look like this: The boss says we must make progress toward a large and difficult-to-measure goal. The leaders of the company choose some proxies that seem to have a plausible connection to the goal.

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See also optimization mindset Einstein, Albert, 77 election fraud alleged in 2020 presidential election, xi Electronic Frontier Foundation (EFF), 119–20 Electronic Privacy Information Center (EPIC), 150 Emanuel, Ezekiel J., 243 encryption, xv, 13, 72, 116, 127–29, 134 Encyclopaedia Britannica, 195 “End of Food, The,” Soylent reported to be, 9 engineers, 6, 10–15, 28–30, 31–33 environment vs. factory farms, 20–21 epistocracy, democracy vs., 66–68 ethics and AI, 165–66 ethics and politics of technological change, xvi–xvii, xx–xxi, xxiii European Commission, 136 European Data Protection Authorities, 145 European model of capitalism, 181 European Union antitrust actions against Google, 228–29 GDPR data protection, 142–45, 147, 238, 241 social safety nets, 185–86 Vestager’s roadblocks to big tech, 252–53, 255 Everyday Sexism Project, 220 expert rulers’ incentive to maintain their status, 68 externalities, xxvi, 48, 73, 200, 260 extremists right to use hate speech in the US, 189–90 FAccT/ML (Fairness, Accountability, and Transparency in Machine Learning), 89 Facebook acquisition of Instagram, 229 business model, xxvii, 18–19 Cambridge Analytica scandal, 37, 128, 146–47, 254 Christchurch, New Zealand, terrorist attack livestreamed, viral, removed, 189 data mining, 118–19 DeepFace system, 161, 162 deleting derogatory statements about men, 188 as digital civic square, 21 and end-to-end encryption, 127–28 FTC complaints against, 150, 253 global audience for truth or lies, 192–93 lawsuit for using facial recognition tech, 46–47 market dominance of, 227 Oversight Board of Facebook, 213–16 plan to comply with GDPR, 145 population of users nearly double that of China, 188–89 #StopProfitForHate campaign vs., 224–25 study of privacy settings, 137 Terms of Service excerpt, 118–19 Zuckerberg appears before House committee, 64–65 See also big tech platforms facial recognition aerial surveillance systems, 112 deep learning, 161–62 downstream applications, 17 Facebook sued for using, 46–47 human supervision requirement, 85 loss of liberties in a democratic society, 125 modern revival of physiognomy, 249 paradox, 113–15 prioritizing optimization vs., 15, 17 protecting celebrities from stalkers, 111–12 women and dark-skinned people, 113 factory farming as a success disaster, 20–21 Factory Investigating Commission, New York, 55 fairness, xxxiii, 20, 73, 88–94, 97, 99, 101, 103, 104, 106–8, 166, 237 Family Educational Rights and Privacy Act (FERPA), 140 famines as man-made political disasters, 74 Federal Bureau of Investigation (FBI), 128–29, 134–35 Federal Communications Commission (FCC), 58, 228 Federal Constitutional Court of Germany, 143 Federal Trade Commission (FTC), 118, 150–51, 228, 253 financial sector, 163–64, 254–55 First Amendment of the Constitution of the United States, 189, 191–92, 214, 216, 217 Flexner, Abraham, 244–45 Floyd, George, murder of, 69 Foot, Philippa, 155 Ford Pinto’s design flaw, 36–37 Foster, Bill, 52 Foucault, Michel, 122–23 free speech and the internet, 187–230 overview, 187–91 beyond self-regulation, 216–21 Christchurch, New Zealand, terrorist attack livestreamed, 189 collision of free speech with democracy and dignity, 198–202 creating a more competitive marketplace, 227–30 exceptions to free speech, 217 foreign interests with election-related advertising as an exception, 225 the future of platform immunity, 221–26 hate speech, 187–91, 200–201, 218 online efforts to regulate speech, 219–21 speech and its consequences, 191–97 Twitter’s suspension of Trump, xi-xii, 187–88 See also creating an alternative future freedom of expression, 198–99 Furman, Jason, 184 Galetti, Beth, 79–80, 99 Gates, Bill, 183 Gebru, Timnit, 112–13, 250 gender bias in recruiting system, 81–82, 83, 100–1 General Data Protection Regulation (GDPR), 142–45, 147, 149, 238, 241, 255 Germany, 143, 217–18 Gibbons, Jack, 258 gig economy workers, 47–49 Gillibrand, Kirsten, 151 Gingrich, Newt, 259 Glickman, Aaron, 243 Go, AI playing, 157 goals, of algorithmic models, 15–16, 18–21, 34–37 “Goals Gone Wild” (Ordóñez), 34–37 Gonzalez, Lorena, 48, 95 Goodhart’s Law, 19 Goodrow, Cristos, 33–34 Google AI ethics board dissolved, 166 data mining, 117 differential privacy technology used by, 131–32 employees protest sale of AI tech, 17 engineers actively unionizing, 180 European Commission lawsuit against, 136 Founders’ Award, 28 Gebru, firing of, 250 Google Buzz launch, 120 management by OKRs, 32–33 market dominance of, 227, 228 OpenSocial specification, 256 partnership with Apple, 113, 141 Pichai appears before House committee, 64–65 state attorneys general filing suit against, 253 Google Buzz, 120 Gore, Al, 59–60 governance, 66–68, 69–72, 105–7, 263–64 government AI-related taxes on businesses, 182–84 Clipper Chip technology, 115–16 companies helping to manage consequences of AI, 184 creating an agency responsible for citizen privacy rights, 150–51 data collection by public institutions, 140–42, 151 developing a new relationship with tech sector, 241 legitimacy of, 68 reasons for involvement in free speech on the internet, 221–26 tax-related subsidies for businesses, 179 See also regulations GPT-2 and GPT-3, OpenAI’s language models, 233–37 greedy algorithms, 12–13 Greenspan, Alan, 61 gross domestic product (GDP), 173 Group Insurance Commission (GIC) of Massachusetts, 130 Grove, Andy, 51 “Guerrilla Open Access Manifesto,” xxii–xxiii Hacker News website, 8 hackers computer scientists as, 21–22 Hacker News, 8 influencing political arena, 46 iPhones’ back door as a challenge to, 135 life hacking website, 14 marriage of capitalists and, 28, 52, 68 Hall, Margeret, 250 happiness in life, importance of, 167, 168 Harvard University Data Privacy Lab, 130–31 Hashemi, Madhi, 250 Hastings, Reed, 5–6 hate speech, 187–91, 200–201, 218, 224–25 Hawley, Josh, 223 Health Insurance Portability and Accountability Act of 1996 (HIPAA), 129, 140, 246 hedge funds using AI, 163–64 Hertzberg, Robert, 95 High Bar for Talent, Amazon’s, 79–80 High Performance Computing and Communications Act (1991), 59–60 Hinton, Geoff, 164 Hoffman, Reid, xxviii, 39, 51–52 Holmes, Elizabeth, xxx Holt, Rush, 52 Hong Kong protests in 2019, 125 Hooked on Phonics, 150 Horowitz, Ben, 42 Houghton, Amo, 259 Human Development Index (HDI), 173 human intelligence compared to machine intelligence, 158–59.

Radical Technologies: The Design of Everyday Life
by Adam Greenfield
Published 29 May 2017

The first is simply that they derive value from its being a proprietary trade secret, or believe that they do. They think that it gives them a competitive advantage, and they don’t want rivals nullifying that advantage by copying it. That part is straightforward enough. But the second reason is that, like all such metrics, these stats can be juked: Branch’s algorithm is subject to Goodhart’s Law, the principle that “when a measure becomes a target, it ceases to be useful as a measure.”66 In other words, they believe that if it became more widely known just how their algorithm arrived at its determinations, it would be easier for unreliable people to act in ways that would fool it into classifying them as trustworthy.

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See Google de Certeau, Michel, 311 Deleuze, Gilles, 148, 211 dematerialization, 11 Demnig, Gunter, 72 de Monchaux, Nicholas, 101 Demos, 246 Deutsche Bank, 278–9 The Dialectic of Sex (Firestone), 191 El Diario (newspaper), 109 Dick, Philip K., 83, 244 digital fabrication, 85–114 digital rights management software, DRM, 292, 295 DiscusFish/F2 Pool mining pools, 139 distributed applications, 115, 147, 149, 163 distributed autonomous organizations, 161–81, 288, 302 distributed consensus, 126 distributed ledgers, 117, 137, 160, 293 Department of Motor Vehicles (DMV), generically, 158 Dodge Charger, 216–17, 221 döner, 71 “Double Bubble Trouble” (M.I.A.), 295 drones, 103, 188, 220, 277–8, 283, 295 DropCam, 281 Dubner, Stephen J., 237 dugnad, 170 Dunning-Kruger syndrome, 260 Dutch East India Company, the, 165 Easterbrook, Steve, 195 Edo, 69 Elemental Technologies, 281 Elephant and Castle Shopping Centre, 110 Eisenman, Peter, 70 Embassy of the United States, Beijing, 51 Eno, Brian, 238 Equal Credit Opportunity Rights, 248 Ethereum/Ether, 148–50, 152–4, 162–3, 168, 175–7, 179 Ethical Filament Foundation, 99 Ethiopia, 194 euro (currency), 100, 131, 136 “eventual consistency,” 134 Existenzminimum, 103 Expedia, 134 EZPass, 59 fablabs, 95, 100, 109–10 faceblindness, 67–8 Facebook, 69, 220–1, 227, 229, 232, 252, 275–9, 281, 284 Aquila autonomous aircraft, 278 Free Basics, 278 Instagram, 278 opacity of Trending News algorithm, 212, 252–3 Fadell, Tony, 276 false positive, truth value, 217, 235, 249 Family Assistance Plan, FAP, 204 Fan Hui, 268 feature engineering, 218 Federal Trade Commission, 248 FedEx, 278 Filabot, 98 Fillod, Odile, 107 Financial Times (newspaper), 177 FindFace software, 240–2 Firestone, Shulamith, 191 Fitbit Charge wearable device, 197 Five Hundred and Seven Mechanical Movements (Brown), 103 Flaxman, Seth, 250–1 foamed aluminum, 95 Ford Mustang, 216–17 Forrester, Jay, 56 Fortune Magazine, 257 Foucault, Michel, 35, 70, 160 Freakonomics (Levitt and Dubner), 237 Frey, Carl Benedikt, 194 Fully Automated Luxury Communism, 90, 111, 190, 289 gallium arsenide, 47 Galloway, Anne, 82 gambiarra, 291 Garrett, Matthew, 43 General Data Protection Regulation, 249 General Public License, 103 Genesis Block, 125, 139 genetic algorithms, 239, 253 gender of pedestrians, as determined by algorithm, 239 as performance, 239–40 of virtual assistants, 39 geofencing, 27 Gershenfeld, Neil, 95 Ghost Gunner, 108 Giger, H. R., 219 GitHub code repository, 242, 274, 281 “glassholes,” 84, 276 Global Village Construction Set, 103 go (game), 263–6 Goodhart’s Law, 247 Goodman, Bryce, 250–1 Google, 18, 24, 37–40, 46, 66, 69, 73–4, 76–8, 80, 84, 193, 212, 218–20, 247, 254, 264, 275, 276, 278, 281, 284 Boston Dynamics robotics division, 276 Chrome browser, 275 Daydream virtual reality headset, 275 Deep Dream, 80, 219 DeepMind, 264–5, 270, 276, 281 driverless cars, 193, 220 Glass augmented reality headset, 66, 73–4, 76–8, 80, 275 Home interface device, 38–40 Image Search, 218 Mail, 275 Maps, 24 Nest home automation division, 275–6 Nest thermostat, 275–6 Play, 18 Plus social network, 276 search results, 212 Sidewalk Labs, 276 Gladwell, Malcolm, 237 Glaser, Will, 220 Global Positioning System, 4, 16, 21, 26, 51, 67 Graeber, David, 205 Guangdong, 179 Guardian (newspaper), 276 Guattari, Félix, 148 Gu Li, 265 Hagakure, 267 Haldane, Andy, 194 Halo (game), 39 Hannah-Arendt-Strasse, 70 haptics, 16 Harman, Graham, 48 hash value, 123–4, 128–30 Hashcash, 121 hashing algorithm, 123 head-up displays, 66–7 Hearn, Mike, 179 Heat List, Chicago Police Department program, 230–1, 233, 235–6, 244 heroin, 228 heterotopias, 70 high-density polyethylene plastic filament, HDPE, 99 Hitachi Corporation, 197 Hollerith machines, 61 hooks, bell, 311 HR analytics, 199 Hungarian pengo, 120, 122 iaido, 266 iaijutsu, 266 IBM, 263 ideology of ease, 42 infrapolitics, 311 ING, bank, 262 input neurons, 215 Instagram.

Capitalism 4.0: The Birth of a New Economy in the Aftermath of Crisis
by Anatole Kaletsky
Published 22 Jun 2010

On closer inspection, however, the proposed monetarist rules turned out to be far from simple. There were many ways of measuring, and even defining, money, which often gave conflicting answers about whether the printing presses should be accelerated or slowed. Moreover, a perversity that came to be known as Goodhart’s Law showed that when central bankers measured money in a particular way and set this as a target, financial markets would quickly hoard or dump that kind of money, thereby guaranteeing the breakdown of whatever relationship had previously existed between this definition of money and inflation.12 To make matters worse, businesspeople, financiers, and ordinary citizens outside America had relied since the war on the gold-backed dollar as a gauge to value their own national currencies, which had proved unreliable and volatile in countries such as Italy, France, and Britain.

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Gamble, Andrew Geithner, Tim General Electric General Theory (Keynes) George, Lloyd Germany and rebalancing global growth Global governance issues Globalization competition and overview predictions on end protectionism and rebalancing growth stabilization and See also Capitalism 4.0/global consequences Gold standard abandonment Britain Capitalism 2 and confidence in paper money and effects future and overview significance of U.S. Goldberg, Michael Goldman Sachs Goodhart’s Law Government legitimacy Government-market relationship overview Government role education privatization areas regalian responsibilities U.S./Europe comparisons Government stimulus, postcrisis about doubt and opposition to support for See also Economic recovery/2009 government response Great Depression causes/handling comparisons to description Keynes and transition and Great Moderation Bernanke’s speech and demand management reinvention and description/effects Great Society Greenspan, Alan crisis of 2007-09 and criticism of on flaw in beliefs housing boom/bust “irrational exuberance,” market fundamentalism and Rand and regulation and stock market crash and GSEs (Government Sponsored Enterprises) government guarantees and public-private status and GSEs (Government Sponsored Enterprises) seizure effects “justification” for Lehman Brothers failure and Paulson and short sellers and Halifax Bank of Scotland (HBOS)-Lloyds merger Harcourt, Geoffrey Hayek, Friedrich background ideas unpredictability and Health/pension entitlements Health reform about conservatives and U.S. consumer spending and (fig.)

Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor
by John Kay
Published 24 May 2004

When governments set targets for schools and hospitals, they face the same problem: the information needed to determine the targets appropriately is held by people in schools and hospitals, not people in government departments. Lenin claimed to have found the answer to this problem: "seize the decisive link." 10 Because the information required to control the system completely is extensive and impossible to obtain, the center must focus on a few supposedly key variables. But these are subject to "Goodhart's Law'' 11 -any measure adopted as a target changes its meaning. If corporate executives receive bonuses related to earnings per share, then earnings per share will rise, but whether the business is better or more valuable is quite another question. The inevitable result of these processes is the proliferation of targets.

WTF?: What's the Future and Why It's Up to Us
by Tim O'Reilly
Published 9 Oct 2017

That is, if everyone is employed, there is no barrier to moving from job to job, and the only way to hang on to employees is to pay them more, which employers necessarily compensated themselves for by raising prices, in a continuing spiral of higher wages and higher prices. As Blyth notes, every intervention is subject to Goodhart’s Law: “Targeting any variable long enough undermines the value of the variable.” Coupled with the end of the Bretton Woods system, a gold exchange standard anchored to the US dollar, the commitment to full employment led to skyrocketing inflation. Inflation is good for debtors—it makes goods such as housing much cheaper, because you repay a fixed dollar amount of debt with future dollars that are worth much less.

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See also open source software Freeware Summit (1998), 15–16, 19 Fried, Limor, 369–70, 371–72 Friedl, Jeffrey, 120–21 Friedman, Milton, 240 future effect of individual decisions, 13 Apple Stores, 321–22 business model map for, 65–70 gravitational cores and gradually attenuating influence, 65 inventing the future, 46–47, 153–54 living in, prior to even distribution, 19, 23, 29, 316 questions about, 300 seeing via innovators in the present, 14 and worker augmentation, 69 “Future of Firms, The” (Kilpi), 89 Gage, John, 28 Gall, John, 106 Gates, Bill, 17, 307, 360 Gebbia, Joe, 97–98 Gelsinger, Pat, 13 General Electric (GE), 241, 249, 303 General Theory of Employment, Interest, and Money (Keynes), 271–72 Generation Z, 341–42 genetic programming, 106 Getaround, 85 GiveDirectly, 305 global brain. See Internet; World Wide Web Global Entrepreneurship Summit, 315 Global Network Navigator (GNN), 28–29, 38–39, 79–81, 89, 276 GNU Manifesto, The (Stallman), 6 Goodhart’s Law, 239 Good Jobs Strategy (Ton), 197 Goodman, George, 278 Google, 103 algorithmic curation of information, 89 Android phones, 52, 101 and cloud computing, 113 continuous improvement process, 119 corpus for language researchers, 155–56 discovery of hidden intelligence in web links, 39 economic impact of, 290–91 and fake news, 202–7, 215–17 importance of, 51–52 Knowledge Graph, 158 on Linux operating system, 24 and machine learning, 335 Moto X phone, 82–83 as native web application, 30–31 as network of people and advertisers, 64 NSF grant for, 132 and online mapping dominance, 127–28 pay-per-click ad auction, 161–62 and public sentiment about privacy, 82 Site Reliability Engineering/DevOps, 123 stock-based compensation, 280, 289–90 Street View cars collecting data, 33, 34–35 Google AlphaGo, x Google Book Search, 170–71 Google Finance, 126 Google Glass, xviii–xix, 344–45 Google Maps, xiii, 127–28 Google Photos, 166 Google Search, 34, 43–44, 156–59, 165 Gordon, Robert, 243 Gov 2.0 Summit and Expo (2009 and 2010), Washington DC, 128–31 government, 187–89, 249–50, 269, 270–73.

The Alignment Problem: Machine Learning and Human Values
by Brian Christian
Published 5 Oct 2020

This also shares some resemblance to the regularization method known as “early stopping”; see Yao, Rosasco, and Caponnetto, “On Early Stopping in Gradient Descent Learning.” For further discussion, in an AI safety context, about when “a metric which can be used to improve a system is used to such an extent that further optimization is ineffective or harmful,” see Manheim and Garrabrant, “Categorizing Variants of Goodhart’s Law.” 5. “£13.3m Boost for Oxford’s Future of Humanity Institute,” http://www.ox.ac.uk/news/2018-10-10-£133m-boost-oxford’s-future-humanity-institute. 6. Huxley, Ends and Means. 7. For a recent general-audience discussion on gender bias in medicine, see Perez, Invisible Women. For academic literature on the subject, see, e.g., Mastroianni, Faden, and Federman, Women and Health Research, and Marts and Keitt, “Foreword.”

…

“Making Learning Fun: A Taxonomy of Intrinsic Motivations for Learning.” In Aptitude, Learning and Instruction III: Conative and Affective Process Analyses, edited by Richard E. Snow and Marshall J. Farr, 223–53. Hillsdale, NJ: Lawrence Erlbaum Associates, 1987. Manheim, David, and Scott Garrabrant. “Categorizing Variants of Goodhart’s Law.” arXiv Preprint arXiv:1803.04585, 2019. Manning, Christopher. “Lecture 2: Word Vector Representations: Word2vec,” April 3, 2017. https://www.youtube.com/watch?v=ERibwqs9p38. Manning, Christopher D., and Hinrich Schütze. Foundations of Statistical Natural Language Processing. MIT Press, 1999.

The Precipice: Existential Risk and the Future of Humanity
by Toby Ord
Published 24 Mar 2020

Stuart Russell (2014) likens it to a common issue in optimization: “A system that is optimizing a function of n variables, where the objective depends on a subset of size k<n, will often set the remaining unconstrained variables to extreme values; if one of those unconstrained variables is actually something we care about, the solution found may be highly undesirable.” Alignment researchers liken the situation to Goodhart’s Law (Goodhart, 1975): “Any observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” This law was originally proposed to think about the problems of setting targets that correlate with what we really want. While the targets may get met, they often cease to correspond with what we ultimately cared about in the process. 92 This could come up in one of two ways.

Transport for Humans: Are We Nearly There Yet?
by Pete Dyson and Rory Sutherland
Published 15 Jan 2021

Created by Ogilvy for Deutsche Bahn, when German Instagram users search for glamorous destinations, an algorithm shows them an attraction of similar beauty much closer to home. Figure 20. Travel can be reframed to promote domestic destinations, with social media enabling smart and timely targeting. 5. Design for perception first Recall Goodhart’s Law: quantification means that a lot of investment is spent improving performance metrics. In comparison, much less effort is needed to improve perceptual ones. This insight is known to proponents of Kano theory, a Japanese model of product development.37 It states that products have ‘delight attributes’ that make us extremely happy but are tangential to what the product is designed to do.

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't
by Nate Silver
Published 31 Aug 2012

As pointed out by the Nobel Prize–winning economist Robert Lucas37 in 1976, the past data that an economic model is premised on resulted in part from policy decisions in place at the time. Thus, it may not be enough to know what current policy makers will do; you also need to know what fiscal and monetary policy looked like during the Nixon administration. A related doctrine known as Goodhart’s law, after the London School of Economics professor who proposed it,38 holds that once policy makers begin to target a particular variable, it may begin to lose its value as an economic indicator. For instance, if the government artificially takes steps to inflate housing prices, they might well increase, but they will no longer be good measures of overall economic health.

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“Skip,” 417n GDP, 482 forecasting of, 180, 181–83, 182, 186n, 190, 194, 198, 199, 200–201, 202–3 growth in, vs. job growth, 189 Gehringer, Charlie, 84, 85 German Peasants’ War, 4 Germany, 2, 115, 120, 210 Germany, East, 52 Giambi, Jason, 99 GIGO (garbage in, garbage out), 289 GISS temperature record, 393–95 Giuliani, Giampaolo, 143, 144–45, 146, 476 Gladwell, Malcolm, 53 global cooling, 399–400 global financial crisis, 11, 16, 20, 30–36, 39–43, 118–19, 329 failure to predict, 181, 327 global population, growth of, 212 global warming, 13 causality and, 372–73 Climategate and, 408 contrarianism and, 380 Copenhagen conference on, 378–80 IPCC report on, see International Panel on Climate Change (IPCC) predictions of, 373–76, 393, 397–99, 401–6, 402, 507 self-interest and, 380 skepticism of, 377, 380, 383, 384–85 use of term, 376, 377n Goldman Sachs, 24n, 184–85, 199, 364 gonorrhea, 222 Goodhart’s law, 188 Google, 264, 290–92 creative culture at, 291 Google searches, 200, 290–91 Gorbachev, Mikhail, 50, 51, 52, 160 Gore, Al, 11, 67, 68, 381–82, 381, 385, 403, 514, 469 government spending, 42, 186n GPS, 174–75, 219 Graham, Benjamin, 364 Grand Forks, N.

The Tyranny of Nostalgia: Half a Century of British Economic Decline
by Russell Jones
Published 15 Jan 2023

Monetary growth was further swollen both by the fact that with the elimination of capital controls British banks could now both borrow from abroad and repatriate cash held overseas and by what became known as ‘distress borrowing’: the tendency for firms to borrow more in an attempt to stay in business. Monetary targeting fell foul of Goodhart’s law: the notion that when a measure becomes a target, it tends to lose its usefulness.4 Hence, in subsequent versions of the MTFS, M3 was supplemented by various other monetary targets (both narrow and broad), and in the end set aside entirely in favour of M0, or notes and coins in circulation. This happened despite it seeming absurd that, in a complex and mature economy, inflation could be determined by what amounted to individuals’ small change.

Stigum's Money Market, 4E
by Marcia Stigum and Anthony Crescenzi
Published 9 Feb 2007

In the 1970s, as inflation rates and nominal interest rates both soared, a host of new financial instruments were created: money market funds, negotiable order of withdrawal (NOW) accounts, Super-NOW accounts, money market deposit accounts (MMDAs), consumer CDs, and so on. To keep pace with the rapidly evolving financial landscape, the Fed had to keep redefining its measures of money supply (Table 9.1). In this environment, M1 quickly ran afoul of Goodhart’s law, which a Bank of England official phrased as follows: “If you create a monetary aggregate and start targeting your system by it, before you know where you are, it will change out of all recognition; and you will have to create another one—exactly what happened in the U.S. and in the U.K.” For the Fed, the introduction of NOW accounts proved to be a classic case in point.

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Breakdown of the Relationship of Borrowed Reserves to the Funds Rate Once a sense of stability was restored to the market after the 1987 crash, the Fed tried to restore borrowed reserves as its central operating procedure. It succeeded only partially as the once-predictable link between borrowed reserves and the funds rate had, by then, become the latest victim of Goodhart’s law. Once the Fed began to base policy on this relationship, the demand curve for borrowed reserves became unstable. This problem is illustrated in Figure 9.2, which plots borrowings at the discount window against the average fed funds rate for each two-week reserve maintenance period from February 1984 through June 1989.

Extreme Money: Masters of the Universe and the Cult of Risk
by Satyajit Das
Published 14 Oct 2011

When the government targeted one measure of money supply, other measures changed unexpectedly. As Charles Goodhart, an English economist and central banker, identified: “observed statistical regularity will tend to collapse once pressure is placed upon it for control purposes.” Christopher Fildes, an English journalist, restated Goodhart’s law: “It’s all very well when anthropologists observe the savages, but all bets are off when the savages start observing the anthropologists.”20 Reaganomics flirted with supply side theory, advocated by little known economist Arthur Laffer. Large tax cuts would stimulate economic growth, expanding the tax base and offsetting the lost revenue from lower tax rates.

Money and Government: The Past and Future of Economics
by Robert Skidelsky
Published 13 Nov 2018

In both cases, the strategy of credible gradual disinflation broke down, with inflation being reversed by shock therapy which imposed a huge cost on output and employment. Analysts pointed to the instability of the demand for money. Both the 1970s and 1980s saw continued enormous swings in velocity, which made money growth a poor predictor of future prices and income. Charles Goodhart enunciated his famous ‘law’ that any established relationship between money and prices breaks down as soon as the attempt is made to exploit it for control purposes. But the flaw lay with the new monetary theory itself: there was never sufficient belief in the pronouncements of the monetary authorities to make disinflation a relatively painless exercise.